{"id":"W7099267569","doi":"","title":"HEC Montreal DEVIANCE FROM IDEAL: WHEN HOMOGENEOUS BOARDS MAKE MISTAKES","year":2016,"lang":"en","type":"article","venue":"","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deviance (statistics); Harm; Homogeneous; Association (psychology); On board","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003495376,0.0001235604,0.0001208821,0.00002950652,0.00002930633,0.00003921327,0.0001150371,0.00006676457,0.001242219],"category_scores_gemma":[0.00001264773,0.00008786903,0.00004025616,0.00004643399,0.0000180544,0.00005560209,0.000008667508,0.00003928772,0.0002835505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003380079,"about_ca_system_score_gemma":0.000009026502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004035166,"about_ca_topic_score_gemma":0.00129302,"domain_scores_codex":[0.9993893,0.00001076596,0.0001557098,0.00014719,0.0001029962,0.0001940691],"domain_scores_gemma":[0.999604,0.00004825573,0.00001666112,0.0002217376,0.00002848,0.00008080029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004551547,0.00006603339,0.007828223,0.00009535271,0.0003010283,0.00007411961,0.002329828,0.06450636,0.01535291,0.001719448,0.1319468,0.7757344],"study_design_scores_gemma":[0.00308895,0.00008216555,0.01122558,0.0003187034,0.00006591911,0.00002662339,0.0001302674,0.0852863,0.02384452,0.00537158,0.8689369,0.001622474],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04389987,0.002837371,0.6007501,0.001563361,0.001280006,0.0003404535,0.0002493571,0.003105726,0.3459737],"genre_scores_gemma":[0.9652762,0.0002351086,0.01921854,0.0002291587,0.0001486086,0.00001315858,0.00001728119,0.00005594907,0.014806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9213763,"threshold_uncertainty_score":0.9996708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021921205321805,"score_gpt":0.1872916085342325,"score_spread":0.1770723964810144,"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."}}