{"id":"W2593845480","doi":"","title":"A COMPARISON OF REGRESSION MODELS FOR INCIDENT RATE PREDICTION IN A CANADIAN POWER COMPANY","year":2010,"lang":"en","type":"article","venue":"","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Econometrics; Regression analysis; Regression; Power (physics); Linear regression; Statistics; Computer science; Economics; Mathematics","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":[0.009649156,0.001411032,0.001264584,0.00222357,0.001371534,0.002125458,0.003272822,0.00109699,0.001714008],"category_scores_gemma":[0.01760076,0.0006271944,0.001555463,0.002447397,0.0004417205,0.0007549303,0.0005269616,0.001559656,0.0004933016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01666734,"about_ca_system_score_gemma":0.01456377,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9486064,"about_ca_topic_score_gemma":0.9036704,"domain_scores_codex":[0.9979479,0.0008771832,0.0001032223,0.0003587272,0.0004334726,0.0002794911],"domain_scores_gemma":[0.9836038,0.011042,0.0005007805,0.0003504196,0.004128252,0.0003747208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003419443,0.00130338,0.1788348,0.0003575289,0.00107489,0.0001821411,0.0006078845,0.6943301,0.001144865,0.002566051,0.01090285,0.1052761],"study_design_scores_gemma":[0.00008271127,0.0002238138,0.0542834,0.0000351868,0.0002225364,0.00002427263,0.0002670508,0.942906,0.0004586989,0.0003555524,0.001076226,0.00006458446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750073,0.001353419,0.01595504,0.00111982,0.0001174026,0.0001472075,0.002386349,0.0005290105,0.003384378],"genre_scores_gemma":[0.9811301,0.0008336749,0.01054048,0.0001102526,0.00003483246,0.00007985031,0.00303408,0.000109788,0.004126905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05139357,"threshold_uncertainty_score":0.1209306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.095054454218806,"score_gpt":0.4148906577773159,"score_spread":0.3198362035585099,"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."}}