{"id":"W2586815467","doi":"10.1002/atr.1442","title":"Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Pedestrian; Statistics; Logit; Crash; Poison control; Mixed logit; Bayesian probability; Logistic regression; Transport engineering; Econometrics; Geography; Computer science; Mathematics; Engineering; Medicine; Medical emergency","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001189369,0.0001346901,0.0003114162,0.0002151899,0.00003082609,0.00000452662,0.0000923118,0.00007350616,0.00003052461],"category_scores_gemma":[0.00001797359,0.000105458,0.0001245192,0.0001762661,0.00003239695,0.0003581489,0.00000184053,0.0001246494,0.000001337807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001271061,"about_ca_system_score_gemma":0.00004549539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002257092,"about_ca_topic_score_gemma":0.009952702,"domain_scores_codex":[0.9988763,0.00001918661,0.0006536991,0.0001023425,0.0001790315,0.0001693997],"domain_scores_gemma":[0.9995307,0.0000537435,0.0001514909,0.00007930623,0.00008487712,0.00009985503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006632332,0.00004798546,0.004912961,0.00005287825,0.00003139511,0.00001761803,0.002673532,0.949015,0.02102326,0.000007760894,0.00001418752,0.02154018],"study_design_scores_gemma":[0.003050076,0.0003001088,0.9729565,0.0004540663,0.00006385708,0.00001576736,0.0006046527,0.01072674,0.01106309,0.0003962279,0.00009192908,0.0002770064],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7619072,0.00003967631,0.2375393,0.000125448,0.0002017431,0.0001010198,0.00003142413,0.00003209956,0.00002208409],"genre_scores_gemma":[0.9966721,0.00008572593,0.003143657,0.000007278808,0.00004601871,0.000004370589,0.000004883588,0.00002049342,0.00001544912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9680435,"threshold_uncertainty_score":0.5553842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007562011366126702,"score_gpt":0.221851140666207,"score_spread":0.2142891293000803,"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."}}