[{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-091809327c957bc6b7163857","output_id":"71ab6a8297a3c3d607ba7fe78051bba0bd8648ac868a78b50d2984fc53e1274c","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n0\",\"n1\",\"n2\",\"n3\",\"n4\",\"n5\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340)\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 317\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 410\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":312,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 312\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[\"n5\",\"n6\",\"n7\"],\"condition\":null,\"course\":null,\"evidence\":\"333, or340\",\"id\":\"n5\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"333\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n8\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"declared in Capstone Certificate in Actuarial Science\",\"course\":null,\"evidence\":\"declared in Capstone Certificate in Actuarial Science\",\"id\":\"n9\",\"kind\":\"condition\"},{\"children\":[\"n0\",\"n8\",\"n9\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340), graduate/professional standing, or declared in Capstone Certificate in Actuarial Science\",\"id\":\"n10\",\"kind\":\"any\"}],\"notes\":[\"STAT 340 is mentioned in requirements_text but not present in linked_courses; treated as a verbatim condition leaf requiring review.\",\"STAT 333 is mentioned as '333' in requirements_text but linked as STAT 333; treated as a course node based on linked_courses context.\"],\"root\":\"n10\",\"status\":\"needs_review\"}","error":"Node n0 references itself; remove the self-reference.\nNode n5 references itself; remove the self-reference.\nCycle reaches node n0; requirement graphs must be trees.\nCycle reaches node n5; requirement graphs must be trees.","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-091809327c957bc6b7163857","output_id":"71ab6a8297a3c3d607ba7fe78051bba0bd8648ac868a78b50d2984fc53e1274c","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus.\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Prerequisite knowledge in statistical inference, regression analysis, and mathematical foundations of business analytics.\"},{\"evidence\":[{\"course_id\":\"ECON 410\",\"field\":\"description\",\"quote\":\"Statistical methods used in applied economic research. Topics include: survey methods and data sources; multiple regression and analysis of variance; econometric estimation; forecasting and policy applications.\"},{\"course_id\":\"MATH/STAT 310\",\"field\":\"description\",\"quote\":\"Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing, and theoretical properties of statistical inference.\"}],\"text\":\"Background in econometric estimation, hypothesis testing, and theoretical statistical properties.\"}],\"search_phrases\":[\"statistical learning actuarial risk\",\"predictive model diagnostics\",\"variable selection techniques\",\"non-supervised learning actuarial\",\"regression model assessment\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Building and assessing linear and nonlinear predictive models.\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"variable and model selection\"}],\"text\":\"Performing variable and model selection.\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"non-supervised learning techniques\"}],\"text\":\"Applying non-supervised learning techniques.\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"diagnostics and assessment of predictive models\"}],\"text\":\"Conducting diagnostics and assessment of predictive models.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"linear and nonlinear models\"}],\"text\":\"Linear and nonlinear models\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"diagnostics and assessment of predictive models\"}],\"text\":\"Diagnostics and assessment of predictive models\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"variable and model selection\"}],\"text\":\"Variable and model selection\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"non-supervised learning techniques\"}],\"text\":\"Non-supervised learning techniques\"}]}","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-091809327c957bc6b7163857","output_id":"71ab6a8297a3c3d607ba7fe78051bba0bd8648ac868a78b50d2984fc53e1274c","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"8da9c7a36c85c73f4a4e97713f817847b813c2d31e7bb40104d4b4f631a651ea","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n0\",\"n1\",\"n2\",\"n3\",\"n4\",\"n5\",\"n6\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340), graduate/professional standing, or declared in Capstone Certificate in Actuarial Science\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 317\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 410\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":312,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 312\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 340\",\"id\":\"n6\",\"kind\":\"course\"}],\"notes\":[\"Missing condition nodes for 'graduate/professional standing' and 'declared in Capstone Certificate in Actuarial Science' which are verbatim conditions in the requirements text but not course nodes.\",\"Missing node for the 'any' grouping of the course prerequisites.\",\"Missing node for the 'any' grouping of the standing/certificate options.\",\"Root node n0 is an 'any' node but only lists course children; the structure is incomplete relative to the full requirements_text.\"],\"root\":\"n0\",\"status\":\"needs_review\"}","error":"Node n0 references itself; remove the self-reference.\nNode n5: evidence 'STAT 333' must quote an exact source substring.\nNode n6: evidence 'STAT 340' must quote an exact source substring.\nCycle reaches node n0; requirement graphs must be trees.","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"8da9c7a36c85c73f4a4e97713f817847b813c2d31e7bb40104d4b4f631a651ea","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"},{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"}],\"text\":\"Linear and nonlinear regression models, model diagnostics, and variable selection techniques.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R.\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"Utilizes the R programming language.\"}],\"text\":\"Proficiency in the R programming language for statistical computing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference\"}],\"text\":\"Probability theory, statistical inference, and simulation methods.\"}],\"search_phrases\":[\"actuarial risk modeling statistical learning\",\"predictive model diagnostics actuarial science\",\"non-supervised learning techniques risk assessment\",\"variable selection methods actuarial applications\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Statistical learning theory and methods for analyzing and modeling risks.\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Diagnostics, assessment, variable selection, and non-supervised learning techniques.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"title\",\"quote\":\"ACTUARIAL STATISTICS FOR RISK MODELING\"},{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Introduces statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Linear and nonlinear models, diagnostics, variable selection, and non-supervised learning.\"}]}","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"8da9c7a36c85c73f4a4e97713f817847b813c2d31e7bb40104d4b4f631a651ea","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"be35b8f69f46fc1cce717f849c6a79b50bfb2993705c9a5dccd727e7c7915811","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n8\",\"n9\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340), graduate/professional standing, or declared in Capstone Certificate in Actuarial Science\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340)\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 410\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":312,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 312\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"333\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n8\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"declared in Capstone Certificate in Actuarial Science\",\"course\":null,\"evidence\":\"declared in Capstone Certificate in Actuarial Science\",\"id\":\"n9\",\"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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"be35b8f69f46fc1cce717f849c6a79b50bfb2993705c9a5dccd727e7c7915811","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"},{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"}],\"text\":\"Linear and nonlinear regression models, model diagnostics, and variable selection techniques.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R.\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"Utilizes the R programming language.\"}],\"text\":\"Proficiency in the R programming language for statistical computing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference\"}],\"text\":\"Probability theory, statistical inference, and simulation methods.\"}],\"search_phrases\":[\"actuarial risk modeling statistical learning\",\"predictive model diagnostics actuarial science\",\"non-supervised learning techniques risk assessment\",\"variable selection methods actuarial applications\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Statistical learning theory and methods for analyzing and modeling risks.\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Diagnostics, assessment, variable selection, and non-supervised learning techniques.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"title\",\"quote\":\"ACTUARIAL STATISTICS FOR RISK MODELING\"},{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Introduces statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Linear and nonlinear models, diagnostics, variable selection, and non-supervised learning.\"}]}","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"be35b8f69f46fc1cce717f849c6a79b50bfb2993705c9a5dccd727e7c7915811","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-789789da373eecc1ff75f626","output_id":"09fdba6ced99a238d6ae59c8a6a47724918890547d06a215c22c43626c6da4dd","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n8\",\"n9\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340), graduate/professional standing, or declared in Capstone Certificate in Actuarial Science\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340)\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 410\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":312,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 312\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"333\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n8\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"declared in Capstone Certificate in Actuarial Science\",\"course\":null,\"evidence\":\"declared in Capstone Certificate in Actuarial Science\",\"id\":\"n9\",\"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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-789789da373eecc1ff75f626","output_id":"09fdba6ced99a238d6ae59c8a6a47724918890547d06a215c22c43626c6da4dd","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"},{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"}],\"text\":\"Linear and nonlinear regression models, model diagnostics, and variable selection techniques.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R.\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"Utilizes the R programming language.\"}],\"text\":\"Proficiency in the R programming language for statistical computing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference\"}],\"text\":\"Probability theory, statistical inference, and simulation methods.\"}],\"search_phrases\":[\"actuarial risk modeling statistical learning\",\"predictive model diagnostics actuarial science\",\"non-supervised learning techniques risk assessment\",\"variable selection methods actuarial applications\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Statistical learning theory and methods for analyzing and modeling risks.\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Diagnostics, assessment, variable selection, and non-supervised learning techniques.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"title\",\"quote\":\"ACTUARIAL STATISTICS FOR RISK MODELING\"},{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Introduces statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Linear and nonlinear models, diagnostics, variable selection, and non-supervised learning.\"}]}","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-789789da373eecc1ff75f626","output_id":"09fdba6ced99a238d6ae59c8a6a47724918890547d06a215c22c43626c6da4dd","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"3cd9a665ed1b94bcf340fe295a98785b7d3bf0d40dddb2b4ff660f1984dcc662","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n8\",\"n9\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340), graduate/professional standing, or declared in Capstone Certificate in Actuarial Science\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\"],\"condition\":null,\"course\":null,\"evidence\":\"(GEN BUS 317,ECON 410,STAT/MATH 310,STAT 312,333, or340)\",\"id\":\"n1\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 410\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":312,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 312\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"333\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"graduate/professional standing\",\"course\":null,\"evidence\":\"graduate/professional standing\",\"id\":\"n8\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"declared in Capstone Certificate in Actuarial Science\",\"course\":null,\"evidence\":\"declared in Capstone Certificate in Actuarial Science\",\"id\":\"n9\",\"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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"3cd9a665ed1b94bcf340fe295a98785b7d3bf0d40dddb2b4ff660f1984dcc662","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"},{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"}],\"text\":\"Linear and nonlinear regression models, model diagnostics, and variable selection techniques.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R.\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"Utilizes the R programming language.\"}],\"text\":\"Proficiency in the R programming language for statistical computing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference\"}],\"text\":\"Probability theory, statistical inference, and simulation methods.\"}],\"search_phrases\":[\"actuarial risk modeling statistical learning\",\"predictive model diagnostics actuarial science\",\"non-supervised learning techniques risk assessment\",\"variable selection methods actuarial applications\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Statistical learning theory and methods for analyzing and modeling risks.\"},{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Diagnostics, assessment, variable selection, and non-supervised learning techniques.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"title\",\"quote\":\"ACTUARIAL STATISTICS FOR RISK MODELING\"},{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"}],\"text\":\"Introduces statistical learning theory and methods for analyzing and modeling risks in actuarial applications.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"ACTSCI 640\",\"field\":\"description\",\"quote\":\"Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.\"}],\"text\":\"Linear and nonlinear models, diagnostics, variable selection, and non-supervised learning.\"}]}","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"3cd9a665ed1b94bcf340fe295a98785b7d3bf0d40dddb2b4ff660f1984dcc662","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_09c6ee1bc749ac38c1a7d335","course_id":"ACTSCI 640","catalog_version_id":"c49d54142efd0a8e8fc3933449f096416029830f8dbee25000df802a58f756a8","record_version_id":"258eaffa1ce419716c31379b486edf3935234eadd75c9bf17b87119df332dccb","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"3cd9a665ed1b94bcf340fe295a98785b7d3bf0d40dddb2b4ff660f1984dcc662","section":"student_summary","status":"valid","value_json":"{\"context_hash\":\"efb41ff57fcfcf8a4be8ec04843a00c3a5eeeeaed802c3ebcc5f58624ae572c3\",\"course_id\":\"ACTSCI 640\",\"current_instructors\":[{\"instructor_uid\":\"instructor_897686e4d69a5b75903adc7a\",\"message\":\"No course-specific reviews available\",\"name\":\"Peng Shi\",\"review_status\":\"no_course_reviews\",\"rmp_instructor_id\":\"rmp:1877077\",\"summary\":[{\"citations\":[{\"course_id\":\"ACTSCI 640\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"source_record\":{\"entity_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1252\",\"type\":\"grade\"},{\"course_id\":\"ACTSCI 640\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"source_record\":{\"entity_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Fall 2024: 3.53 GPA, 88.2% A/AB (n=17 letter grades); Fall 2025: 3.62 GPA, 71.4% A/AB (n=21 letter grades).\"}]}],\"difficulty_workload\":[],\"errors\":[],\"historical_context\":[],\"message\":\"No course-specific reviews available\",\"offered\":true,\"profile_hash\":\"672f506f2fc2f46a071b9777f4a92cc197b2ccdeef25590e9285146d8c7e7f90\",\"quick_take\":[{\"citations\":[{\"course_id\":\"ACTSCI 640\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"},{\"course_id\":\"ACTSCI 640\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"},{\"course_id\":\"ACTSCI 640\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Spring 2025: 3.35 GPA, 70.6% A/AB (n=17 letter grades); Fall 2025: 3.62 GPA, 71.4% A/AB (n=21 letter grades); Spring 2026: 3.65 GPA, 80.0% A/AB (n=30 letter grades).\"}],\"student_experience\":[],\"task_hash\":\"74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68\",\"teaching_history\":[{\"citations\":[{\"course_id\":\"ACTSCI 640\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"source_record\":{\"entity_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1252\",\"type\":\"grade\"},{\"course_id\":\"ACTSCI 640\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"source_record\":{\"entity_id\":\"a0513339-00cf-33d4-b56e-809eda16ff6d\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"}],\"text\":\"PENG SHI is recorded teaching in Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.\"}],\"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}]