[{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"c5763870b219f36b81d78511254bb914aa19f1e8a8e2f1e68aed60d303a8f0f2","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[],\"condition\":\"Graduate/professional standing\",\"course\":null,\"evidence\":\"Graduate/professional standing\",\"id\":\"n0\",\"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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"c5763870b219f36b81d78511254bb914aa19f1e8a8e2f1e68aed60d303a8f0f2","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[],\"search_phrases\":[\"time series analysis\",\"forecasting models\",\"ARIMA models\",\"statistical modeling\",\"difference equations\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Theory and application of discrete time series models\"}],\"text\":\"Theory and application of discrete time series models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Principles of iterative model building\"}],\"text\":\"Principles of iterative model building\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Identification, fitting, diagnostic checking of models\"}],\"text\":\"Identification, fitting, and diagnostic checking of models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Seasonal model application to forecasting\"}],\"text\":\"Seasonal model application to forecasting\"}],\"summary\":{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"title\",\"quote\":\"APPLIED TIME SERIES ANALYSIS, FORECASTING AND CONTROL I\"},{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Theory and application of discrete time series models illustrated with forecasting problems\"}],\"text\":\"STAT 701 covers the theory and application of discrete time series models, focusing on forecasting, model building, and diagnostic checking.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Representation of dynamic relations by difference equations\"}],\"text\":\"Dynamic relations by difference equations\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Autoregressive integrated Moving Average models\"}],\"text\":\"Autoregressive integrated Moving Average (ARIMA) models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Seasonal model application\"}],\"text\":\"Seasonal model application\"}]}","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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"c5763870b219f36b81d78511254bb914aa19f1e8a8e2f1e68aed60d303a8f0f2","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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"77ddc068e49a5b46a6edaa302178bec6ddb3c970db6b7e8fc42006fa98b79bf6","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[],\"condition\":\"Graduate/professional standing\",\"course\":null,\"evidence\":\"Graduate/professional standing\",\"id\":\"n0\",\"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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"77ddc068e49a5b46a6edaa302178bec6ddb3c970db6b7e8fc42006fa98b79bf6","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[],\"search_phrases\":[\"time series analysis\",\"forecasting models\",\"ARIMA models\",\"statistical modeling\",\"difference equations\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Theory and application of discrete time series models\"}],\"text\":\"Theory and application of discrete time series models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Principles of iterative model building\"}],\"text\":\"Principles of iterative model building\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Identification, fitting, diagnostic checking of models\"}],\"text\":\"Identification, fitting, and diagnostic checking of models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Seasonal model application to forecasting\"}],\"text\":\"Seasonal model application to forecasting\"}],\"summary\":{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"title\",\"quote\":\"APPLIED TIME SERIES ANALYSIS, FORECASTING AND CONTROL I\"},{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Theory and application of discrete time series models illustrated with forecasting problems\"}],\"text\":\"STAT 701 covers the theory and application of discrete time series models, focusing on forecasting, model building, and diagnostic checking.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Representation of dynamic relations by difference equations\"}],\"text\":\"Dynamic relations by difference equations\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Autoregressive integrated Moving Average models\"}],\"text\":\"Autoregressive integrated Moving Average (ARIMA) models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Seasonal model application\"}],\"text\":\"Seasonal model application\"}]}","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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"77ddc068e49a5b46a6edaa302178bec6ddb3c970db6b7e8fc42006fa98b79bf6","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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"778ce63e5edfdf12d6a817554ad1830297bc9578fc0e8b178638efa8dfcaf7b2","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[],\"condition\":\"Graduate/professional standing\",\"course\":null,\"evidence\":\"Graduate/professional standing\",\"id\":\"n0\",\"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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"778ce63e5edfdf12d6a817554ad1830297bc9578fc0e8b178638efa8dfcaf7b2","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[],\"search_phrases\":[\"time series analysis\",\"forecasting models\",\"ARIMA models\",\"statistical modeling\",\"difference equations\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Theory and application of discrete time series models\"}],\"text\":\"Theory and application of discrete time series models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Principles of iterative model building\"}],\"text\":\"Principles of iterative model building\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Identification, fitting, diagnostic checking of models\"}],\"text\":\"Identification, fitting, and diagnostic checking of models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Seasonal model application to forecasting\"}],\"text\":\"Seasonal model application to forecasting\"}],\"summary\":{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"title\",\"quote\":\"APPLIED TIME SERIES ANALYSIS, FORECASTING AND CONTROL I\"},{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Theory and application of discrete time series models illustrated with forecasting problems\"}],\"text\":\"STAT 701 covers the theory and application of discrete time series models, focusing on forecasting, model building, and diagnostic checking.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Representation of dynamic relations by difference equations\"}],\"text\":\"Dynamic relations by difference equations\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Autoregressive integrated Moving Average models\"}],\"text\":\"Autoregressive integrated Moving Average (ARIMA) models\"},{\"evidence\":[{\"course_id\":\"STAT 701\",\"field\":\"description\",\"quote\":\"Seasonal model application\"}],\"text\":\"Seasonal model application\"}]}","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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"778ce63e5edfdf12d6a817554ad1830297bc9578fc0e8b178638efa8dfcaf7b2","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_98c8f7223e5e4f977f8032c8","course_id":"STAT 701","catalog_version_id":"854403f3f4254a78dc94a24d12bc50f64f68e59a70a7edbdeec41d5068f026f6","record_version_id":"c3667663d1bd701b0e88f49701cc53b28df84612dfd63fed00534c187a900850","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"778ce63e5edfdf12d6a817554ad1830297bc9578fc0e8b178638efa8dfcaf7b2","section":"student_summary","status":"valid","value_json":"{\"context_hash\":\"9f5f9b5d7c515fa96f206433167f0233f33c4cb5b28ede4a34320334841b3e42\",\"course_id\":\"STAT 701\",\"current_instructors\":[{\"instructor_uid\":\"instructor_7eca2811645c15f0e8db796e\",\"message\":\"No course-specific reviews available\",\"name\":\"Christopher Geoga\",\"review_status\":\"no_course_reviews\",\"rmp_instructor_id\":null,\"summary\":[{\"citations\":[{\"course_id\":\"STAT 701\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"source_record\":{\"entity_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1242\",\"type\":\"grade\"},{\"course_id\":\"STAT 701\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"source_record\":{\"entity_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Fall 2023: 4.00 GPA, 100.0% A/AB (n=11 letter grades); Spring 2025: 3.67 GPA, 77.8% A/AB (n=18 letter grades).\"}]}],\"difficulty_workload\":[],\"errors\":[],\"historical_context\":[],\"message\":\"No course-specific reviews available\",\"offered\":true,\"profile_hash\":\"5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02\",\"quick_take\":[{\"citations\":[{\"course_id\":\"STAT 701\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1224\",\"type\":\"grade\"},{\"course_id\":\"STAT 701\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1242\",\"type\":\"grade\"},{\"course_id\":\"STAT 701\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Spring 2022: 3.88 GPA, 94.1% A/AB (n=17 letter grades); Fall 2023: 4.00 GPA, 100.0% A/AB (n=11 letter grades); Spring 2025: 3.67 GPA, 77.8% A/AB (n=18 letter grades).\"}],\"student_experience\":[],\"task_hash\":\"74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68\",\"teaching_history\":[{\"citations\":[{\"course_id\":\"STAT 701\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"source_record\":{\"entity_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1242\",\"type\":\"grade\"},{\"course_id\":\"STAT 701\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"source_record\":{\"entity_id\":\"3932776f-ebf8-3a23-b066-29b1784b69b2\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"}],\"text\":\"CHRISTOPHER GEOGA is recorded teaching in Fall 2023, Spring 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}]