[{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"a357b5ae58470a5d9c9fcf0c8f3f55f1db3fcefe12b73b70610e9897069a36ef","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n0\",\"n1\"],\"condition\":null,\"course\":null,\"evidence\":\"Graduate/professional standing or declared in Mathematics VISP (graduate or dissertator)\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":0,\"minimum_grade\":null,\"subjects\":[\"Graduate/professional standing\"],\"timing\":\"prior\"},\"evidence\":\"Graduate/professional standing\",\"id\":\"n1\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"declared in Mathematics VISP (graduate or dissertator)\",\"course\":null,\"evidence\":\"declared in Mathematics VISP (graduate or dissertator)\",\"id\":\"n2\",\"kind\":\"condition\"}],\"notes\":[\"Graduate/professional standing is treated as a condition leaf as it is not a specific course in linked_courses.\"],\"root\":\"n0\",\"status\":\"needs_review\"}","error":"Node n0 references itself; remove the self-reference.\nCycle reaches node n0; requirement graphs must be trees.\nUnreachable nodes: n2; connect all conditions and exclusions to the root.","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"a357b5ae58470a5d9c9fcf0c8f3f55f1db3fcefe12b73b70610e9897069a36ef","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[],\"search_phrases\":[\"randomized linear algebra\",\"randomized Kaczmarz\",\"stochastic gradient descent\",\"randomized SVD\",\"matrix completion\",\"compressive sensing\",\"random sketching\",\"inverse problems\",\"scientific computing\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Systematic study of these modern methods of randomized linear algebra solvers will be provided, presenting mathematical backgrounds, algorithms, and concrete applications.\"}],\"text\":\"Study of randomized linear algebra solvers, algorithms, and applications\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Core theoretical topics include randomized Kaczmarz and its generalization to stochastic gradient descent, randomized singular value decomposition, random sketching, matrix completion, and compressive sensing\"}],\"text\":\"Understanding core theoretical topics in randomized linear algebra\"}],\"summary\":{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"title\",\"quote\":\"RANDOMIZED LINEAR ALGEBRA AND APPLICATIONS\"},{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Systematic study of these modern methods of randomized linear algebra solvers will be provided, presenting mathematical backgrounds, algorithms, and concrete applications.\"}],\"text\":\"MATH 718 covers randomized linear algebra solvers, algorithms, and applications including Kaczmarz, SVD, and sketching.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"randomized Kaczmarz and its generalization to stochastic gradient descent\"}],\"text\":\"Randomized Kaczmarz and stochastic gradient descent\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"randomized singular value decomposition\"}],\"text\":\"Randomized singular value decomposition\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"random sketching, matrix completion, and compressive sensing\"}],\"text\":\"Random sketching, matrix completion, and compressive sensing\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"inverse problems, machine learning and scientific computing\"}],\"text\":\"Applications in inverse problems, machine learning, and scientific computing\"}]}","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"a357b5ae58470a5d9c9fcf0c8f3f55f1db3fcefe12b73b70610e9897069a36ef","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"da7de5e3a3de561dc8e2c59c908a6e8e248cf6f1fb1921041231c4c0322c7f7d","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"Graduate/professional standing or declared in Mathematics VISP (graduate or dissertator)\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":\"Graduate/professional standing\",\"course\":null,\"evidence\":\"Graduate/professional standing\",\"id\":\"n1\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"declared in Mathematics VISP (graduate or dissertator)\",\"course\":null,\"evidence\":\"declared in Mathematics VISP (graduate or dissertator)\",\"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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"da7de5e3a3de561dc8e2c59c908a6e8e248cf6f1fb1921041231c4c0322c7f7d","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[],\"search_phrases\":[\"randomized linear algebra\",\"randomized Kaczmarz\",\"stochastic gradient descent\",\"randomized SVD\",\"matrix completion\",\"compressive sensing\",\"random sketching\",\"inverse problems\",\"scientific computing\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Systematic study of these modern methods of randomized linear algebra solvers will be provided, presenting mathematical backgrounds, algorithms, and concrete applications.\"}],\"text\":\"Study of randomized linear algebra solvers, algorithms, and applications\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Core theoretical topics include randomized Kaczmarz and its generalization to stochastic gradient descent, randomized singular value decomposition, random sketching, matrix completion, and compressive sensing\"}],\"text\":\"Understanding core theoretical topics in randomized linear algebra\"}],\"summary\":{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"title\",\"quote\":\"RANDOMIZED LINEAR ALGEBRA AND APPLICATIONS\"},{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Systematic study of these modern methods of randomized linear algebra solvers will be provided, presenting mathematical backgrounds, algorithms, and concrete applications.\"}],\"text\":\"MATH 718 covers randomized linear algebra solvers, algorithms, and applications including Kaczmarz, SVD, and sketching.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"randomized Kaczmarz and its generalization to stochastic gradient descent\"}],\"text\":\"Randomized Kaczmarz and stochastic gradient descent\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"randomized singular value decomposition\"}],\"text\":\"Randomized singular value decomposition\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"random sketching, matrix completion, and compressive sensing\"}],\"text\":\"Random sketching, matrix completion, and compressive sensing\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"inverse problems, machine learning and scientific computing\"}],\"text\":\"Applications in inverse problems, machine learning, and scientific computing\"}]}","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"da7de5e3a3de561dc8e2c59c908a6e8e248cf6f1fb1921041231c4c0322c7f7d","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-789789da373eecc1ff75f626","output_id":"fe4474233644760d8bddf010997fe34420b2cb689cc4c6ce9574656196979711","section":"requirements","status":"needs_review","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"Graduate/professional standing or declared in Mathematics VISP (graduate or dissertator)\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":\"Graduate/professional standing\",\"course\":null,\"evidence\":\"Graduate/professional standing\",\"id\":\"n1\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"declared in Mathematics VISP (graduate or dissertator)\",\"course\":null,\"evidence\":\"declared in Mathematics VISP (graduate or dissertator)\",\"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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-789789da373eecc1ff75f626","output_id":"fe4474233644760d8bddf010997fe34420b2cb689cc4c6ce9574656196979711","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[],\"search_phrases\":[\"randomized linear algebra\",\"randomized Kaczmarz\",\"stochastic gradient descent\",\"randomized SVD\",\"matrix completion\",\"compressive sensing\",\"random sketching\",\"inverse problems\",\"scientific computing\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Systematic study of these modern methods of randomized linear algebra solvers will be provided, presenting mathematical backgrounds, algorithms, and concrete applications.\"}],\"text\":\"Study of randomized linear algebra solvers, algorithms, and applications\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Core theoretical topics include randomized Kaczmarz and its generalization to stochastic gradient descent, randomized singular value decomposition, random sketching, matrix completion, and compressive sensing\"}],\"text\":\"Understanding core theoretical topics in randomized linear algebra\"}],\"summary\":{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"title\",\"quote\":\"RANDOMIZED LINEAR ALGEBRA AND APPLICATIONS\"},{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Systematic study of these modern methods of randomized linear algebra solvers will be provided, presenting mathematical backgrounds, algorithms, and concrete applications.\"}],\"text\":\"MATH 718 covers randomized linear algebra solvers, algorithms, and applications including Kaczmarz, SVD, and sketching.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"randomized Kaczmarz and its generalization to stochastic gradient descent\"}],\"text\":\"Randomized Kaczmarz and stochastic gradient descent\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"randomized singular value decomposition\"}],\"text\":\"Randomized singular value decomposition\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"random sketching, matrix completion, and compressive sensing\"}],\"text\":\"Random sketching, matrix completion, and compressive sensing\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"inverse problems, machine learning and scientific computing\"}],\"text\":\"Applications in inverse problems, machine learning, and scientific computing\"}]}","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-789789da373eecc1ff75f626","output_id":"fe4474233644760d8bddf010997fe34420b2cb689cc4c6ce9574656196979711","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"1699be0093a28903b5d01e84eac887f607cab522a19d56fac3bb4fd1311c9cb7","section":"requirements","status":"valid","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\"],\"condition\":null,\"course\":null,\"evidence\":\"Graduate/professional standing or declared in Mathematics VISP (graduate or dissertator)\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":\"Graduate/professional standing\",\"course\":null,\"evidence\":\"Graduate/professional standing\",\"id\":\"n1\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"declared in Mathematics VISP (graduate or dissertator)\",\"course\":null,\"evidence\":\"declared in Mathematics VISP (graduate or dissertator)\",\"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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"1699be0093a28903b5d01e84eac887f607cab522a19d56fac3bb4fd1311c9cb7","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[],\"search_phrases\":[\"randomized linear algebra\",\"randomized Kaczmarz\",\"stochastic gradient descent\",\"randomized SVD\",\"matrix completion\",\"compressive sensing\",\"random sketching\",\"inverse problems\",\"scientific computing\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Systematic study of these modern methods of randomized linear algebra solvers will be provided, presenting mathematical backgrounds, algorithms, and concrete applications.\"}],\"text\":\"Study of randomized linear algebra solvers, algorithms, and applications\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Core theoretical topics include randomized Kaczmarz and its generalization to stochastic gradient descent, randomized singular value decomposition, random sketching, matrix completion, and compressive sensing\"}],\"text\":\"Understanding core theoretical topics in randomized linear algebra\"}],\"summary\":{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"title\",\"quote\":\"RANDOMIZED LINEAR ALGEBRA AND APPLICATIONS\"},{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"Systematic study of these modern methods of randomized linear algebra solvers will be provided, presenting mathematical backgrounds, algorithms, and concrete applications.\"}],\"text\":\"MATH 718 covers randomized linear algebra solvers, algorithms, and applications including Kaczmarz, SVD, and sketching.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"randomized Kaczmarz and its generalization to stochastic gradient descent\"}],\"text\":\"Randomized Kaczmarz and stochastic gradient descent\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"randomized singular value decomposition\"}],\"text\":\"Randomized singular value decomposition\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"random sketching, matrix completion, and compressive sensing\"}],\"text\":\"Random sketching, matrix completion, and compressive sensing\"},{\"evidence\":[{\"course_id\":\"MATH 718\",\"field\":\"description\",\"quote\":\"inverse problems, machine learning and scientific computing\"}],\"text\":\"Applications in inverse problems, machine learning, and scientific computing\"}]}","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"1699be0093a28903b5d01e84eac887f607cab522a19d56fac3bb4fd1311c9cb7","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_4c518c7aee577750986dc7c8","course_id":"MATH 718","catalog_version_id":"6abd77afa0c5cc2fb52089990f2a655229eb67f24bcc7121e0cd542d31bdf6a8","record_version_id":"5ef8fe7b9a2fd26f32db90bdab88adcc266317127d664b0e4ce293e4cd77b735","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"1699be0093a28903b5d01e84eac887f607cab522a19d56fac3bb4fd1311c9cb7","section":"student_summary","status":"valid","value_json":"{\"context_hash\":\"7345a1322e1af335d58d62a9e43e21bddb6ad34753a8bc0c431f5a16f3b1c1e0\",\"course_id\":\"MATH 718\",\"current_instructors\":[],\"difficulty_workload\":[],\"errors\":[],\"historical_context\":[],\"message\":\"No course-specific reviews available\",\"offered\":false,\"profile_hash\":\"5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02\",\"quick_take\":[{\"citations\":[{\"course_id\":\"MATH 718\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"83307b03-51e0-31a1-a5ba-40c4ba2ab211\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1252\",\"type\":\"grade\"},{\"course_id\":\"MATH 718\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"83307b03-51e0-31a1-a5ba-40c4ba2ab211\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Fall 2024: 3.83 GPA, 100.0% A/AB (n=23 letter grades); Spring 2026: 3.69 GPA, 88.5% A/AB (n=26 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}]