[{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-2978ec7e9ac23a465ccaacbb","output_id":"89ab2fd36fb056deeb1c06f8f586bbbf24b607263aad25423b3f42b9584ed923","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371, or graduate/professional standing\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n3\",\"n4\",\"n5\",\"n6\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":240,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 240\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":301,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"301\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":324,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"324\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":371,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"371\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n2\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-2978ec7e9ac23a465ccaacbb","output_id":"89ab2fd36fb056deeb1c06f8f586bbbf24b607263aad25423b3f42b9584ed923","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 240\",\"field\":\"description\",\"quote\":\"Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown\"}],\"text\":\"Data management, wrangling, and R programming\"},{\"evidence\":[{\"course_id\":\"STAT 301\",\"field\":\"description\",\"quote\":\"Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design.\"}],\"text\":\"Statistical distributions, regression, and experimental design\"},{\"evidence\":[{\"course_id\":\"STAT 324\",\"field\":\"description\",\"quote\":\"Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language.\"}],\"text\":\"Hypothesis testing, ANOVA, and model checking\"},{\"evidence\":[{\"course_id\":\"STAT 371\",\"field\":\"description\",\"quote\":\"Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.\"}],\"text\":\"Exploratory data analysis and applied statistical inference\"}],\"search_phrases\":[\"environmental data science machine learning\",\"F&WECOL 458 R programming\",\"STAT 240 301 324 371 prerequisites\",\"environmental sciences data analysis course\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"Machine learning for numerical modeling and data analysis\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets\"}],\"text\":\"Data preparation and visualization using programming tools\"}],\"summary\":{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"title\",\"quote\":\"ENVIRONMENTAL DATA SCIENCE\"},{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"F&WECOL 458 ENVIRONMENTAL DATA SCIENCE introduces machine learning and programming tools for analyzing and visualizing environmental data.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"fundamental machine learning techniques for numerical modeling and data analysis\"}],\"text\":\"Machine learning and numerical modeling\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data\"}],\"text\":\"Data analysis, preparation, and visualization\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"real-world applications for concepts in environmental data science\"}],\"text\":\"Environmental data science applications\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-2978ec7e9ac23a465ccaacbb","output_id":"89ab2fd36fb056deeb1c06f8f586bbbf24b607263aad25423b3f42b9584ed923","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"b573b538441414a96a38e1fecf6e9ca3bbaea3c356ee06b3949bf4d94d55cfa6","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371, or graduate/professional standing\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n3\",\"n4\",\"n5\",\"n6\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":240,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 240\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":301,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 301\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":324,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 324\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":371,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 371\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n2\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","error":"Node n4: evidence 'STAT 301' must quote an exact source substring.\nNode n5: evidence 'STAT 324' must quote an exact source substring.\nNode n6: evidence 'STAT 371' must quote an exact source substring.","model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"b573b538441414a96a38e1fecf6e9ca3bbaea3c356ee06b3949bf4d94d55cfa6","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 240\",\"field\":\"description\",\"quote\":\"Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown\"}],\"text\":\"Data management, wrangling, and R programming\"},{\"evidence\":[{\"course_id\":\"STAT 301\",\"field\":\"description\",\"quote\":\"Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design.\"}],\"text\":\"Statistical distributions, regression, and experimental design\"},{\"evidence\":[{\"course_id\":\"STAT 324\",\"field\":\"description\",\"quote\":\"Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language.\"}],\"text\":\"Hypothesis testing, ANOVA, and model checking\"},{\"evidence\":[{\"course_id\":\"STAT 371\",\"field\":\"description\",\"quote\":\"Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.\"}],\"text\":\"Exploratory data analysis and applied statistical inference\"}],\"search_phrases\":[\"environmental data science machine learning\",\"F&WECOL 458 R programming\",\"STAT 240 301 324 371 prerequisites\",\"environmental sciences data analysis course\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"Machine learning for numerical modeling and data analysis\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets\"}],\"text\":\"Data preparation and visualization using programming tools\"}],\"summary\":{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"title\",\"quote\":\"ENVIRONMENTAL DATA SCIENCE\"},{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"F&WECOL 458 ENVIRONMENTAL DATA SCIENCE introduces machine learning and programming tools for analyzing and visualizing environmental data.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"fundamental machine learning techniques for numerical modeling and data analysis\"}],\"text\":\"Machine learning and numerical modeling\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data\"}],\"text\":\"Data analysis, preparation, and visualization\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"real-world applications for concepts in environmental data science\"}],\"text\":\"Environmental data science applications\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"b573b538441414a96a38e1fecf6e9ca3bbaea3c356ee06b3949bf4d94d55cfa6","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"e148bb14bb3326f306547df42bdd6ec3dc6ad02dd7eec9e7b7da064571f4e133","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371, or graduate/professional standing\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n3\",\"n4\",\"n5\",\"n6\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":240,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 240\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":301,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"301\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":324,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"324\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":371,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"371\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n2\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"e148bb14bb3326f306547df42bdd6ec3dc6ad02dd7eec9e7b7da064571f4e133","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 240\",\"field\":\"description\",\"quote\":\"Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown\"}],\"text\":\"Data management, wrangling, and R programming\"},{\"evidence\":[{\"course_id\":\"STAT 301\",\"field\":\"description\",\"quote\":\"Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design.\"}],\"text\":\"Statistical distributions, regression, and experimental design\"},{\"evidence\":[{\"course_id\":\"STAT 324\",\"field\":\"description\",\"quote\":\"Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language.\"}],\"text\":\"Hypothesis testing, ANOVA, and model checking\"},{\"evidence\":[{\"course_id\":\"STAT 371\",\"field\":\"description\",\"quote\":\"Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.\"}],\"text\":\"Exploratory data analysis and applied statistical inference\"}],\"search_phrases\":[\"environmental data science machine learning\",\"F&WECOL 458 R programming\",\"STAT 240 301 324 371 prerequisites\",\"environmental sciences data analysis course\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"Machine learning for numerical modeling and data analysis\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets\"}],\"text\":\"Data preparation and visualization using programming tools\"}],\"summary\":{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"title\",\"quote\":\"ENVIRONMENTAL DATA SCIENCE\"},{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"F&WECOL 458 ENVIRONMENTAL DATA SCIENCE introduces machine learning and programming tools for analyzing and visualizing environmental data.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"fundamental machine learning techniques for numerical modeling and data analysis\"}],\"text\":\"Machine learning and numerical modeling\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data\"}],\"text\":\"Data analysis, preparation, and visualization\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"real-world applications for concepts in environmental data science\"}],\"text\":\"Environmental data science applications\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"e148bb14bb3326f306547df42bdd6ec3dc6ad02dd7eec9e7b7da064571f4e133","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-789789da373eecc1ff75f626","output_id":"6872683428f399a969b719323e90437633e94fc5218a425370750c87d4a80046","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371, or graduate/professional standing\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n3\",\"n4\",\"n5\",\"n6\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":240,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 240\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":301,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"\\\"STAT 301\\\" in requirements_text\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":324,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"\\\"STAT 324\\\" in requirements_text\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":371,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"\\\"STAT 371\\\" in requirements_text\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n2\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","error":"Node n4: evidence '\"STAT 301\" in requirements_text' must quote an exact source substring.\nNode n5: evidence '\"STAT 324\" in requirements_text' must quote an exact source substring.\nNode n6: evidence '\"STAT 371\" in requirements_text' must quote an exact source substring.","model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-789789da373eecc1ff75f626","output_id":"6872683428f399a969b719323e90437633e94fc5218a425370750c87d4a80046","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 240\",\"field\":\"description\",\"quote\":\"Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown\"}],\"text\":\"Data management, wrangling, and R programming\"},{\"evidence\":[{\"course_id\":\"STAT 301\",\"field\":\"description\",\"quote\":\"Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design.\"}],\"text\":\"Statistical distributions, regression, and experimental design\"},{\"evidence\":[{\"course_id\":\"STAT 324\",\"field\":\"description\",\"quote\":\"Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language.\"}],\"text\":\"Hypothesis testing, ANOVA, and model checking\"},{\"evidence\":[{\"course_id\":\"STAT 371\",\"field\":\"description\",\"quote\":\"Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.\"}],\"text\":\"Exploratory data analysis and applied statistical inference\"}],\"search_phrases\":[\"environmental data science machine learning\",\"F&WECOL 458 R programming\",\"STAT 240 301 324 371 prerequisites\",\"environmental sciences data analysis course\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"Machine learning for numerical modeling and data analysis\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets\"}],\"text\":\"Data preparation and visualization using programming tools\"}],\"summary\":{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"title\",\"quote\":\"ENVIRONMENTAL DATA SCIENCE\"},{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"F&WECOL 458 ENVIRONMENTAL DATA SCIENCE introduces machine learning and programming tools for analyzing and visualizing environmental data.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"fundamental machine learning techniques for numerical modeling and data analysis\"}],\"text\":\"Machine learning and numerical modeling\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data\"}],\"text\":\"Data analysis, preparation, and visualization\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"real-world applications for concepts in environmental data science\"}],\"text\":\"Environmental data science applications\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-789789da373eecc1ff75f626","output_id":"6872683428f399a969b719323e90437633e94fc5218a425370750c87d4a80046","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"6b12f938dfc0a6ee0bfa4f40ffcd2739475d3fc731c29b24dcbc73658c42126f","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371, or graduate/professional standing\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n3\",\"n4\",\"n5\",\"n6\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":240,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 240\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":301,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"301\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":324,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"324\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":371,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"371\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n2\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"6b12f938dfc0a6ee0bfa4f40ffcd2739475d3fc731c29b24dcbc73658c42126f","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 240\",\"field\":\"description\",\"quote\":\"Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown\"}],\"text\":\"Data management, wrangling, and R programming\"},{\"evidence\":[{\"course_id\":\"STAT 301\",\"field\":\"description\",\"quote\":\"Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design.\"}],\"text\":\"Statistical distributions, regression, and experimental design\"},{\"evidence\":[{\"course_id\":\"STAT 324\",\"field\":\"description\",\"quote\":\"Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language.\"}],\"text\":\"Hypothesis testing, ANOVA, and model checking\"},{\"evidence\":[{\"course_id\":\"STAT 371\",\"field\":\"description\",\"quote\":\"Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.\"}],\"text\":\"Exploratory data analysis and applied statistical inference\"}],\"search_phrases\":[\"environmental data science machine learning\",\"F&WECOL 458 R programming\",\"STAT 240 301 324 371 prerequisites\",\"environmental sciences data analysis course\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"Machine learning for numerical modeling and data analysis\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets\"}],\"text\":\"Data preparation and visualization using programming tools\"}],\"summary\":{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"title\",\"quote\":\"ENVIRONMENTAL DATA SCIENCE\"},{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"F&WECOL 458 ENVIRONMENTAL DATA SCIENCE introduces machine learning and programming tools for analyzing and visualizing environmental data.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"fundamental machine learning techniques for numerical modeling and data analysis\"}],\"text\":\"Machine learning and numerical modeling\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data\"}],\"text\":\"Data analysis, preparation, and visualization\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"real-world applications for concepts in environmental data science\"}],\"text\":\"Environmental data science applications\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"6b12f938dfc0a6ee0bfa4f40ffcd2739475d3fc731c29b24dcbc73658c42126f","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"6b12f938dfc0a6ee0bfa4f40ffcd2739475d3fc731c29b24dcbc73658c42126f","section":"student_summary","status":"valid","value_json":"{\"context_hash\":\"fe640bed78945974ed33a8274d161b542af2612e2314f106383ceef0edec518e\",\"course_id\":\"F&WECOL 458\",\"current_instructors\":[],\"difficulty_workload\":[],\"errors\":[],\"historical_context\":[],\"message\":\"No course-specific reviews available\",\"offered\":false,\"profile_hash\":\"5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02\",\"quick_take\":[{\"citations\":[{\"course_id\":\"F&WECOL 458\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"c739741c-609d-32ba-a265-b24fe9dc4dcb\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"},{\"course_id\":\"F&WECOL 458\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"c739741c-609d-32ba-a265-b24fe9dc4dcb\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Spring 2025: 3.97 GPA, 100.0% A/AB (n=16 letter grades); Spring 2026: 3.95 GPA, 97.4% A/AB (n=39 letter grades).\"}],\"student_experience\":[],\"task_hash\":\"74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68\",\"teaching_history\":[],\"term_id\":\"1272\",\"term_name\":\"2026 Fall\",\"version\":2}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-dab8f6acaa72f26086773521","output_id":"8cceaff1714a475a1e75a57d6a410d0130958e5977b66b089171730f356ff72b","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371, or graduate/professional standing\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n3\",\"n4\",\"n5\",\"n6\"],\"condition\":null,\"course\":null,\"evidence\":\"STAT 240,301,324,371\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":240,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 240\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":301,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 301\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":324,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 324\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":371,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 371\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n2\",\"kind\":\"condition\"}],\"notes\":[],\"root\":\"n0\",\"status\":\"parsed\"}","error":"Node n4: evidence 'STAT 301' must quote an exact source substring.\nNode n5: evidence 'STAT 324' must quote an exact source substring.\nNode n6: evidence 'STAT 371' must quote an exact source substring.","model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-dab8f6acaa72f26086773521","output_id":"8cceaff1714a475a1e75a57d6a410d0130958e5977b66b089171730f356ff72b","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 240\",\"field\":\"description\",\"quote\":\"Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown\"}],\"text\":\"Data management, wrangling, and R programming\"},{\"evidence\":[{\"course_id\":\"STAT 301\",\"field\":\"description\",\"quote\":\"Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design.\"}],\"text\":\"Statistical distributions, regression, and experimental design\"},{\"evidence\":[{\"course_id\":\"STAT 324\",\"field\":\"description\",\"quote\":\"Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language.\"}],\"text\":\"Hypothesis testing, ANOVA, and model checking\"},{\"evidence\":[{\"course_id\":\"STAT 371\",\"field\":\"description\",\"quote\":\"Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.\"}],\"text\":\"Exploratory data analysis and applied statistical inference\"}],\"search_phrases\":[\"environmental data science machine learning\",\"F&WECOL 458 R programming\",\"STAT 240 301 324 371 prerequisites\",\"environmental sciences data analysis course\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"Machine learning for numerical modeling and data analysis\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets\"}],\"text\":\"Data preparation and visualization using programming tools\"}],\"summary\":{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"title\",\"quote\":\"ENVIRONMENTAL DATA SCIENCE\"},{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"Introduces fundamental machine learning techniques for numerical modeling and data analysis and modern computer programming tools used to analyze, prepare, and visualize data from common formats of datasets in the field of Earth and environmental sciences.\"}],\"text\":\"F&WECOL 458 ENVIRONMENTAL DATA SCIENCE introduces machine learning and programming tools for analyzing and visualizing environmental data.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"fundamental machine learning techniques for numerical modeling and data analysis\"}],\"text\":\"Machine learning and numerical modeling\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"modern computer programming tools used to analyze, prepare, and visualize data\"}],\"text\":\"Data analysis, preparation, and visualization\"},{\"evidence\":[{\"course_id\":\"F&WECOL 458\",\"field\":\"description\",\"quote\":\"real-world applications for concepts in environmental data science\"}],\"text\":\"Environmental data science applications\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4125898f74a0419b4957be76","course_id":"F&WECOL 458","catalog_version_id":"2c599535515395cbcb6862e8ddffeaab53add5e47efd31f642d59c8d4c7b5b0b","record_version_id":"4f52253b913757fd383318a1c291c20b1ae83b250a3b0669b1afcf610d5bbe29","job_id":"enrich-dab8f6acaa72f26086773521","output_id":"8cceaff1714a475a1e75a57d6a410d0130958e5977b66b089171730f356ff72b","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false}]