{"id":"W1995934506","doi":"10.1016/j.eswa.2008.05.011","title":"Information extraction from syllabi for academic e-Advising","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Syllabus; Information extraction; Extraction (chemistry); Academic advising; Information retrieval; Mathematics education; Higher education; Psychology; Chromatography; Chemistry; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115941,0.0001014292,0.0001353872,0.0001130863,0.0004018795,0.0001087672,0.0004249606,0.00007237044,0.000001884145],"category_scores_gemma":[0.00001539262,0.00008479376,0.00003542,0.0003734515,0.00002668662,0.001412795,0.00003330582,0.00009015171,0.00009683547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005206916,"about_ca_system_score_gemma":0.00006603722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003618135,"about_ca_topic_score_gemma":0.000002726551,"domain_scores_codex":[0.9991365,0.00002039826,0.0002880357,0.000201536,0.0002010421,0.0001525028],"domain_scores_gemma":[0.9989437,0.000114313,0.000196236,0.0005470917,0.0001260329,0.00007263434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001329322,0.0005462929,0.007807649,0.0002913078,0.0008854544,0.00001086813,0.06297033,0.01396611,0.04746055,0.2318836,0.2325206,0.4015243],"study_design_scores_gemma":[0.0005237844,0.000043547,0.0008933198,0.00007556304,0.00001964699,0.0001063525,0.001167399,0.146856,0.001418764,0.0001856809,0.8483505,0.0003594405],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002940369,0.0003901343,0.9949998,0.000415487,0.00009140068,0.0004508899,0.0000326169,0.000222722,0.000456525],"genre_scores_gemma":[0.9095501,0.0000840013,0.08716037,0.0001996796,0.0003992195,0.002061493,0.0002892195,0.00001060002,0.0002452653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9078395,"threshold_uncertainty_score":0.3457789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02437700956399376,"score_gpt":0.2810708853278222,"score_spread":0.2566938757638285,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}