{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002719664,0.00008474138,0.0003495116,0.0005909042,0.00009782796,0.00005135115,0.0003645089,0.0001037692,0.0005571033],"category_scores_gemma":[0.0007272607,0.00005508386,0.0001205409,0.0006195744,0.00005109282,0.0002738294,0.0000333506,0.0001451006,0.00002967809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004242844,"about_ca_system_score_gemma":0.0002028176,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1423092,"about_ca_topic_score_gemma":0.8602626,"domain_scores_codex":[0.9981838,0.00009193752,0.000729872,0.0002714297,0.0004920773,0.0002308613],"domain_scores_gemma":[0.9984511,0.0004407614,0.0001998972,0.0003974161,0.0003068503,0.0002040481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002420248,0.0002388197,0.8594365,0.000005581418,0.00003014101,0.000003104123,0.004709128,0.05121809,0.006602155,0.01932727,0.03501426,0.02317293],"study_design_scores_gemma":[0.0003427579,0.00004936127,0.1253393,0.00001058481,0.000009198596,6.331338e-7,0.00108992,0.8256771,0.001010787,0.04162198,0.004773884,0.00007455079],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682951,0.00003102422,0.02101015,0.001464298,0.0002912711,0.0002274834,0.00003900158,0.00001093509,0.00863076],"genre_scores_gemma":[0.9970224,0.000004963191,0.001731822,0.00006774795,0.0000153399,0.00001135458,0.000007240352,0.000004280257,0.001134851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.774459,"threshold_uncertainty_score":0.8634022,"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."}}