{"id":"W3206542204","doi":"10.3390/futuretransp1030030","title":"Advances in Regression Kriging-Based Methods for Estimating Statewide Winter Weather Collisions: An Empirical Investigation","year":2021,"lang":"en","type":"article","venue":"Future Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Kriging; Computer science; Regression; Regression analysis; Fidelity; Generalization; Variogram; Variance (accounting); Transport engineering; Operations research; Econometrics; Statistics; Engineering; Machine learning; Mathematics; Business","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":[0.009205977,0.0007134675,0.000484178,0.001321316,0.0002648204,0.000709137,0.0008434408,0.0004874009,0.0009555398],"category_scores_gemma":[0.02052889,0.0003215245,0.0005399565,0.002957219,0.0003566998,0.001379945,0.00053692,0.0009451854,0.0002791505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007299992,"about_ca_system_score_gemma":0.001211814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04987474,"about_ca_topic_score_gemma":0.09211258,"domain_scores_codex":[0.9969758,0.001972675,0.0001377976,0.0003029473,0.0005171371,0.00009370784],"domain_scores_gemma":[0.9777819,0.01818643,0.001288443,0.0009920928,0.001637088,0.0001140519],"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":[0.0001744976,0.0008973662,0.6000733,0.0003770458,0.000378041,0.0001618375,0.001801515,0.1935328,0.001303156,0.005535722,0.001023854,0.194741],"study_design_scores_gemma":[0.00002253213,0.0006215138,0.3452221,0.0002453225,0.0002836338,0.000157182,0.003225532,0.640497,0.001354125,0.002357984,0.005931421,0.0000817314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.916908,0.001204246,0.07753375,0.0001769722,0.00001260811,0.0001538551,0.000450953,0.00005775821,0.003501997],"genre_scores_gemma":[0.9340566,0.00159949,0.0627738,0.0000256792,0.000007949653,0.00007984556,0.000658427,0.00001704577,0.0007812473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04987474,"threshold_uncertainty_score":0.09916884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01707037431250632,"score_gpt":0.3434337596009343,"score_spread":0.326363385288428,"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."}}