{"id":"W203542624","doi":"10.2172/1010417","title":"Real-time individualized training vectors for experiential learning.","year":2011,"lang":"en","type":"report","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Federated Co-operatives (Canada)","funders":"","keywords":"Experiential learning; Software deployment; Computer science; Adaptation (eye); Personalized learning; Construct (python library); Training (meteorology); Artificial intelligence; Multimedia; Data science; Human–computer interaction; Machine learning; Mathematics education; Teaching method; Psychology; Cooperative learning; Software engineering; Open learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00218513,0.0006116305,0.0002848526,0.0007780552,0.000172241,0.001298395,0.0008117754,0.000372239,0.007106044],"category_scores_gemma":[0.01746809,0.0002266013,0.0002701835,0.0006189387,0.0003229811,0.001771136,0.001102483,0.0006175573,0.001644748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005293392,"about_ca_system_score_gemma":0.000639013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007099248,"about_ca_topic_score_gemma":0.001339187,"domain_scores_codex":[0.9986333,0.0006192714,0.00009204043,0.0002210858,0.0003790841,0.00005524071],"domain_scores_gemma":[0.9946273,0.002962095,0.000509969,0.001052242,0.0005159769,0.0003323797],"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":[0.001185404,0.00157658,0.02497564,0.0005272577,0.0001135023,0.00007764941,0.0009572936,0.05121157,0.02105328,0.013376,0.007303491,0.8776423],"study_design_scores_gemma":[0.0001373574,0.002749725,0.04532816,0.0001594713,0.00008213133,0.0002561497,0.0007917366,0.8617903,0.03850515,0.0269074,0.02318616,0.0001062698],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.2758788,0.0001731039,0.7001581,0.0003344672,0.0001340296,0.001532256,0.002503445,0.005658571,0.01362713],"genre_scores_gemma":[0.7536891,0.0000955182,0.2390962,0.0000485513,0.00001648342,0.001194141,0.001941186,0.0001513999,0.003767541],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.007106044,"threshold_uncertainty_score":0.02377206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09689278515632678,"score_gpt":0.3163758274421725,"score_spread":0.2194830422858458,"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."}}