{"id":"W2015168688","doi":"10.1016/j.aap.2007.10.007","title":"Power computations in time series analyses for traffic safety interventions","year":2007,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Institute on Alcohol Abuse and Alcoholism","keywords":"Transport engineering; Data collection; Intervention (counseling); Poison control; Time series; Occupational safety and health; Series (stratigraphy); Computer science; Sample (material); Psychological intervention; Engineering; Reliability engineering; Risk analysis (engineering); Statistics; Business; Environmental health; Medicine; Mathematics; Machine learning","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002914352,0.0007242665,0.0009614794,0.001087818,0.0004615814,0.001184581,0.0007233339,0.0007198626,0.003378439],"category_scores_gemma":[0.0279453,0.0004788451,0.0007966214,0.001086519,0.0009082832,0.002220738,0.0008665135,0.001416527,0.0003043976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006254084,"about_ca_system_score_gemma":0.0008777216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003591264,"about_ca_topic_score_gemma":0.003063587,"domain_scores_codex":[0.9989266,0.0006027682,0.00007127032,0.0001215874,0.0002024799,0.00007525981],"domain_scores_gemma":[0.98331,0.01515109,0.0003427382,0.0005457565,0.0005521134,0.00009827755],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001950162,0.00006923803,0.001526041,0.0001693202,0.00009529651,0.0001309069,0.000179533,0.7594123,0.001505268,0.1220439,0.002027449,0.1126457],"study_design_scores_gemma":[0.000006429239,0.00001369417,0.0002182502,0.000007329841,0.00001049704,0.000009956394,0.00001480265,0.9581993,0.0004502106,0.04074193,0.0003233768,0.000004217789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03088267,0.0003774746,0.9663134,0.0003265569,0.00010771,0.00003304249,0.00006101672,0.0002049293,0.001693167],"genre_scores_gemma":[0.8357458,0.0007282621,0.157573,0.000140439,0.0002696194,0.0001852246,0.0001940759,0.0003218888,0.0048419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9970856,"threshold_uncertainty_score":0.01541275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02180676234720868,"score_gpt":0.3271111017830392,"score_spread":0.3053043394358305,"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."}}