{"id":"W4246157030","doi":"10.32920/ryerson.14664270.v1","title":"Regression to the mean correction for Collision Modification Factors","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Collision; Standard deviation; Regression; Statistics; Relative standard deviation; Function (biology); Absolute deviation; Regression analysis; Regression toward the mean; Population; Mathematics; Computer science; Simulation; Demography; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02778612,0.002271425,0.001712476,0.002774493,0.001067529,0.001537355,0.002803584,0.001439022,0.008931436],"category_scores_gemma":[0.1305241,0.001016371,0.003830511,0.004157448,0.0009897065,0.001694994,0.00184285,0.004125908,0.00467034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001571238,"about_ca_system_score_gemma":0.00296965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01744544,"about_ca_topic_score_gemma":0.01110778,"domain_scores_codex":[0.9830358,0.006939459,0.0009551494,0.004534602,0.003638554,0.000896492],"domain_scores_gemma":[0.941381,0.0287315,0.003918762,0.01544442,0.01019279,0.0003313676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000978993,0.0005551392,0.1851413,0.0009055224,0.002963176,0.0005444756,0.001681206,0.1586843,0.01613476,0.03396714,0.03980389,0.55864],"study_design_scores_gemma":[0.0001788538,0.001156164,0.1982752,0.0003130711,0.001088467,0.0007949705,0.0005306758,0.5864265,0.03846966,0.02191273,0.150455,0.0003987541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05252751,0.0005689485,0.9342799,0.0002701861,0.0007371407,0.000564354,0.002340848,0.005814706,0.002896278],"genre_scores_gemma":[0.4029061,0.0005352458,0.566681,0.0004403698,0.000238856,0.001625278,0.004936568,0.005464465,0.01717208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02778612,"threshold_uncertainty_score":0.1469488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03352503157368784,"score_gpt":0.2679917923178403,"score_spread":0.2344667607441525,"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."}}