{"id":"W2292223787","doi":"10.1007/s40670-015-0219-2","title":"Learning Anatomical Structures: a Reinforcement-Based Learning Approach","year":2015,"lang":"en","type":"article","venue":"Medical Science Educator","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Calgary Laboratory Services; University of Victoria; University of Calgary","funders":"","keywords":"Reinforcement learning; Task (project management); Identification (biology); Reinforcement; Computer science; Artificial intelligence; Curriculum; Psychology; Social 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001150886,0.0006305096,0.0005264866,0.0003483307,0.0002833031,0.0005058614,0.001445465,0.0008498483,0.003707551],"category_scores_gemma":[0.004513073,0.0003619829,0.0004164889,0.0002118805,0.0007647561,0.000791742,0.001011986,0.001203273,0.0002634648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005880917,"about_ca_system_score_gemma":0.0009923257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003413683,"about_ca_topic_score_gemma":0.003703062,"domain_scores_codex":[0.9997635,0.00007988745,0.00001329687,0.00007411154,0.00004923653,0.00001992434],"domain_scores_gemma":[0.9976314,0.001780791,0.0001587694,0.0001213373,0.0001943727,0.0001133595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002825267,0.0004228636,0.002204797,0.0001317982,0.00009201832,0.00007939937,0.0002187669,0.7029139,0.009675689,0.01744886,0.0008918488,0.2656375],"study_design_scores_gemma":[0.00003967062,0.0001426964,0.0002400482,0.00001061895,0.00001660257,0.00002072605,0.00001342967,0.9896706,0.001391865,0.008157656,0.0002891944,0.000006982182],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0260714,0.0000673325,0.9709522,0.0001995797,0.00002502172,0.00008028567,0.00001505406,0.0002897423,0.002299491],"genre_scores_gemma":[0.6976609,0.0001451383,0.2993935,0.00009569061,0.00003495808,0.0002535678,0.00003342493,0.00004411679,0.002338753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003707551,"threshold_uncertainty_score":0.01240301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05631190962143705,"score_gpt":0.3647558614860671,"score_spread":0.3084439518646301,"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."}}