{"id":"W2885639781","doi":"10.1177/0361198118786829","title":"A Novel Technique to Identify Hot Zones for Active Commuters’ Crashes","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of British Columbia","funders":"","keywords":"Mahalanobis distance; Context (archaeology); Crash; Bayes' theorem; Multivariate statistics; Computer science; Poison control; Consistency (knowledge bases); Statistics; Bayesian probability; Transport engineering; Engineering; Mathematics; Machine learning; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001299662,0.0007657891,0.0007388217,0.003605163,0.0005815441,0.0008883859,0.001011936,0.0005634513,0.002594911],"category_scores_gemma":[0.004454013,0.0004213011,0.0008672663,0.001380711,0.0003948618,0.0009892696,0.001123417,0.000812355,0.001039205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003373536,"about_ca_system_score_gemma":0.001211307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005891596,"about_ca_topic_score_gemma":0.008126984,"domain_scores_codex":[0.9989452,0.0001755992,0.00006749949,0.0003089372,0.0004149893,0.00008781967],"domain_scores_gemma":[0.9978222,0.0009341031,0.0003233575,0.0002425277,0.0005996806,0.00007824662],"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":[0.0002570774,0.0002366983,0.05768357,0.0004202689,0.0002487297,0.0002653475,0.0008466528,0.03013514,0.02615316,0.01190306,0.003485098,0.8683652],"study_design_scores_gemma":[0.00007645469,0.0004617922,0.09348011,0.0001567302,0.0003060875,0.002652622,0.001049325,0.8348758,0.02650803,0.02172256,0.01852266,0.0001879113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02973701,0.0002955088,0.967021,0.00008071506,0.00004481579,0.0001121742,0.0002418554,0.0006103034,0.001856521],"genre_scores_gemma":[0.3105267,0.0003153266,0.6853071,0.0000733374,0.00009346583,0.0001724948,0.0005800087,0.0001144825,0.002817112],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005891596,"threshold_uncertainty_score":0.01171458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09368125596692253,"score_gpt":0.4016593752943166,"score_spread":0.3079781193273941,"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."}}