{"id":"W1990390858","doi":"10.3115/1599503.1599559","title":"Determining curricular coverage of student contributions to an online discourse environment through the use of latent semantic analysis and term clouds","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Visualization; Latent semantic analysis; Probabilistic latent semantic analysis; Computer science; Term (time); Semantic analysis (machine learning); Tag cloud; Curriculum; Semantic differential; Information retrieval; Data visualization; Semantics (computer science); Data science; Semantic computing; Natural language processing; Artificial intelligence; Semantic Web; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001183459,0.00008801719,0.0002053756,0.00006850849,0.00006225528,0.00008321835,0.0003280191,0.00001842797,0.00001362167],"category_scores_gemma":[0.00001627214,0.00005615027,0.00006561849,0.0003955936,0.00004364188,0.0003201246,0.0001707748,0.00003423645,9.116912e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000165121,"about_ca_system_score_gemma":0.00001191245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002776906,"about_ca_topic_score_gemma":0.00002310428,"domain_scores_codex":[0.9990678,0.00006284676,0.0002944048,0.0002016054,0.0002570298,0.0001162985],"domain_scores_gemma":[0.9992318,0.00003865992,0.0001175356,0.0005008224,0.00004549867,0.00006566235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001679136,0.006747225,0.5471085,0.0000327846,0.001533306,0.00005125297,0.01205823,0.1999423,0.007139028,0.1460788,0.000436494,0.07885525],"study_design_scores_gemma":[0.0003485332,0.0003303324,0.5945126,0.000023495,0.0004307587,0.000002639709,0.000155951,0.4026118,0.0008789639,0.0001841939,0.0003582769,0.0001623927],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4295449,0.000009579479,0.569851,0.0004398139,0.000009189785,0.00007652215,0.00005721409,0.000007990674,0.000003744934],"genre_scores_gemma":[0.9916207,0.0001037976,0.007603073,0.0005769696,0.000008370115,0.000001006158,0.00004731556,0.000001949876,0.00003682226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.562248,"threshold_uncertainty_score":0.2289741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04775242110599091,"score_gpt":0.3581800367804853,"score_spread":0.3104276156744944,"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."}}