{"id":"W3095679794","doi":"10.1155/2020/1751350","title":"Methods for Identifying Truck Crash Hotspots","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Department of Homeland Security; U.S. Department of Transportation","keywords":"Hotspot (geology); Crash; Truck; Normalization (sociology); Baseline (sea); Computer science; Significant difference; Road traffic; Motor vehicle crash; Poison control; Transport engineering; Geographic information system; Statistics; Geography; Engineering; Mathematics; Injury prevention; Cartography; Medicine; Emergency medicine; Automotive engineering; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.00123145,0.0009476484,0.0007602959,0.004552486,0.0005042153,0.0009968067,0.001205757,0.0005602716,0.001852506],"category_scores_gemma":[0.002904076,0.0003611482,0.0006510445,0.002364348,0.0003233659,0.0010206,0.000893848,0.0005479651,0.0008734097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004745391,"about_ca_system_score_gemma":0.0008284805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005472107,"about_ca_topic_score_gemma":0.006860876,"domain_scores_codex":[0.9990539,0.0001600176,0.0000715105,0.0003176415,0.0003207767,0.00007624037],"domain_scores_gemma":[0.9988118,0.000405337,0.000163318,0.0001255515,0.0004539217,0.00004007704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002384814,0.0001495777,0.04845253,0.0004961566,0.0002854921,0.000282674,0.0004009122,0.07026821,0.02117072,0.006652857,0.006737216,0.8448651],"study_design_scores_gemma":[0.00005802518,0.0001271258,0.04521102,0.00007037894,0.0001493812,0.001019602,0.0006478043,0.9022765,0.02241622,0.009162396,0.01875201,0.0001094918],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06411623,0.001384721,0.9273061,0.0001551622,0.0001367391,0.0002950369,0.0009547384,0.002483376,0.003167913],"genre_scores_gemma":[0.456743,0.001112891,0.5348506,0.00008923416,0.0001560415,0.0004028869,0.00193908,0.000168339,0.004537869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005472107,"threshold_uncertainty_score":0.01088047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02679128319318527,"score_gpt":0.3205462749761756,"score_spread":0.2937549917829904,"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."}}