{"id":"W2898829623","doi":"10.1145/3274297","title":"MechanicalHeart","year":2018,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; William Osler Health System; University of Waterloo","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Classifier (UML); Baseline (sea); Active listening; Human heart; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003096825,0.0001440457,0.0001540114,0.0001194554,0.0002623109,0.0001904721,0.002697697,0.00005361579,0.00003397347],"category_scores_gemma":[0.0001552755,0.00010103,0.0001224356,0.0002705607,0.000054884,0.0004862579,0.001265884,0.0003174444,0.0001113131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004045101,"about_ca_system_score_gemma":0.000008252866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002079744,"about_ca_topic_score_gemma":9.021295e-7,"domain_scores_codex":[0.9988316,0.00001551382,0.0002567639,0.000376129,0.0003213654,0.000198594],"domain_scores_gemma":[0.9987735,0.00006119238,0.0002572447,0.0006004487,0.0002601952,0.00004741786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001016106,0.0007921328,0.001810409,0.0001487185,0.0001776574,0.000001942861,0.00535255,0.00008642633,0.1355007,0.3743887,0.1364085,0.3452307],"study_design_scores_gemma":[0.001157623,0.003267298,0.01378202,0.0007981699,0.00003581682,0.0001690189,0.0000886932,0.354006,0.4579464,0.112273,0.0556631,0.0008128671],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9116291,0.000007912692,0.05249783,0.01132252,0.005969807,0.0003724644,9.344625e-7,0.0005977291,0.01760168],"genre_scores_gemma":[0.9580256,7.763887e-7,0.03957279,0.0007551688,0.001058846,0.000006670515,2.807096e-7,0.00001202626,0.0005678821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3539196,"threshold_uncertainty_score":0.5013036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04029947875898483,"score_gpt":0.3288402795906193,"score_spread":0.2885408008316345,"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."}}