{"id":"W2097294082","doi":"10.1162/pres.16.6.603","title":"Fuzzy Set Theory for Performance Evaluation in a Surgical Simulator","year":2007,"lang":"en","type":"article","venue":"PRESENCE Virtual and Augmented Reality","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Computer science; Fuzzy logic; Knot tying; Task (project management); Machine learning; Classifier (UML); Artificial intelligence; Simulation; Engineering; Medicine","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":[0.003246442,0.00009584381,0.000179422,0.00007363118,0.00007389711,0.00001151175,0.00003511223,0.00007794554,0.00007702689],"category_scores_gemma":[0.0004109689,0.00007278386,0.00004395336,0.0001771859,0.00008584964,0.00009668743,0.00002606197,0.0001250462,0.000002789517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004255266,"about_ca_system_score_gemma":0.00003696495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001964215,"about_ca_topic_score_gemma":0.00001102553,"domain_scores_codex":[0.9988476,0.00009487943,0.0002825232,0.0002364444,0.000306969,0.0002315983],"domain_scores_gemma":[0.99877,0.0008344147,0.00004851549,0.0001149336,0.0000952408,0.0001368788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.006684798,0.0003395038,0.08016466,0.00009701025,0.00005116204,0.00002855013,0.001108495,0.002702827,0.0003067068,0.008662776,0.00002840039,0.8998251],"study_design_scores_gemma":[0.02177394,0.0007996815,0.5302463,0.0002934881,0.0001053774,0.00003023727,0.001547175,0.4259219,0.001472309,0.00431351,0.01315944,0.000336601],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916407,0.00009257204,0.0007736148,0.0001797043,0.00006952765,0.0006275258,0.00001052645,0.00003472887,0.006571072],"genre_scores_gemma":[0.9994606,0.00002640609,0.00003264934,0.0001180093,0.00008199308,0.00002395345,0.00006039505,0.000006122529,0.0001898208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8994885,"threshold_uncertainty_score":0.296804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07754252769769382,"score_gpt":0.3813554708646655,"score_spread":0.3038129431669717,"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."}}