{"id":"W2803620450","doi":"","title":"In-house Design and Construction of the Toronto Lap-Nissen Simulator","year":2018,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto General Hospital; University Health Network","funders":"","keywords":"Presentation (obstetrics); Simulation; Engineering; Computer science; Engineering management; Medical education; Medicine; Surgery","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001262386,0.0008843017,0.0002884772,0.0008565256,0.000447186,0.0007124199,0.001266777,0.0004296107,0.01334944],"category_scores_gemma":[0.001725376,0.0004033602,0.0005011119,0.0003086778,0.0005174157,0.0003412158,0.0009775492,0.0004955161,0.00204731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005523007,"about_ca_system_score_gemma":0.002616899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003561074,"about_ca_topic_score_gemma":0.008002914,"domain_scores_codex":[0.9992466,0.0001252784,0.00006686995,0.00006818234,0.0004129598,0.00008016928],"domain_scores_gemma":[0.9992631,0.0001296047,0.00004548482,0.000140751,0.0002457775,0.0001752323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001110794,0.0008176598,0.01723565,0.002354798,0.0001090938,0.005035005,0.003096496,0.07799412,0.3692018,0.02487361,0.02911324,0.4690577],"study_design_scores_gemma":[0.0004334332,0.009484212,0.03444582,0.0005405523,0.0002982356,0.007160977,0.001706792,0.1352741,0.2431447,0.002549528,0.5645608,0.0004007917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1584675,0.0003873842,0.7866805,0.0004049362,0.000759427,0.007548574,0.001436751,0.003539554,0.04077534],"genre_scores_gemma":[0.3224696,0.0006075148,0.6423782,0.00009790845,0.00008816624,0.0019059,0.00149972,0.0004565493,0.03049652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01334944,"threshold_uncertainty_score":0.04465836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0240737098854984,"score_gpt":0.2896506102678895,"score_spread":0.2655769003823911,"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."}}