{"id":"W613678579","doi":"10.1007/978-3-319-06483-3","title":"Advances in artificial intelligence : 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, Montréal, QC, Canada, May 6-9, 2014 : proceedings","year":2014,"lang":"en","type":"book","venue":"","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Graphics; Constraint (computer-aided design); Representation (politics); Knowledge representation and reasoning; Artificial neural network; Robotics; Applications of artificial intelligence; Machine learning; Robot; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001416839,0.001413623,0.001541613,0.002661873,0.001888946,0.007126458,0.00188023,0.001097888,0.1325287],"category_scores_gemma":[0.002836908,0.0006753143,0.0007143615,0.004486934,0.001620362,0.002764661,0.001922462,0.002789351,0.04409244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01038498,"about_ca_system_score_gemma":0.02434452,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3782296,"about_ca_topic_score_gemma":0.6066564,"domain_scores_codex":[0.9988338,0.00006641266,0.00003835138,0.00009196212,0.0008668174,0.0001025827],"domain_scores_gemma":[0.9979013,0.000228181,0.00003782162,0.0001415722,0.001383733,0.0003073245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001247861,0.00001540753,0.0001041401,0.000156631,0.000009123306,0.00001275651,0.00005123383,0.0003314223,0.0002032397,0.006474828,0.9105259,0.08210295],"study_design_scores_gemma":[0.000004150567,0.000004309614,0.0004613904,0.0001171861,0.000007808289,0.00002976156,0.00005763022,0.0004910808,0.0001707858,0.002916236,0.9957289,0.00001059658],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001475874,0.1397974,0.02650661,0.01779719,0.02546667,0.0002663809,0.007185473,0.002980551,0.7785237],"genre_scores_gemma":[0.004654053,0.05462375,0.009753982,0.0009441611,0.001108628,0.00007234886,0.004157101,0.0007688149,0.9239172],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6217704,"threshold_uncertainty_score":0.752056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0799334169035372,"score_gpt":0.3474180044667621,"score_spread":0.2674845875632249,"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."}}