{"id":"W2996901405","doi":"10.1155/2019/9873832","title":"Crash-Prone Section Identification for Mountainous Highways Considering Multi-Risk Factors Coupling Effect","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Coupling (piping); Section (typography); Crash; Identification (biology); Transport engineering; Environmental science; Computer science; Engineering","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":[0.0002925843,0.0007122447,0.0003519247,0.001066839,0.0003227062,0.0005717882,0.0004922255,0.0003804541,0.0008539223],"category_scores_gemma":[0.0008861363,0.0002342361,0.0005751915,0.0004500639,0.0002395014,0.0006869379,0.0005540559,0.0002851156,0.0001109816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003721884,"about_ca_system_score_gemma":0.0005954757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01688513,"about_ca_topic_score_gemma":0.01053848,"domain_scores_codex":[0.9997973,0.00003384295,0.00001000018,0.00006244011,0.00005073441,0.00004568639],"domain_scores_gemma":[0.9997095,0.0000688551,0.00007089677,0.000018736,0.000105326,0.00002655018],"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":[0.0001314427,0.00006088146,0.1082185,0.0001403525,0.0001310452,0.0008019482,0.0004100987,0.8341811,0.009414772,0.003897099,0.0006191254,0.0419938],"study_design_scores_gemma":[0.000002615797,0.00003577,0.01930733,0.000006663842,0.0000270679,0.00007699691,0.000124432,0.9787386,0.0007493141,0.0007394767,0.0001787956,0.00001298805],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7349478,0.000224419,0.2620364,0.00005498884,0.00001577358,0.00004005979,0.000139204,0.0001884226,0.002352895],"genre_scores_gemma":[0.9952143,0.00006306606,0.004070995,0.000004673917,0.000004542562,0.00001113896,0.00009197417,0.0000042509,0.0005350645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01688513,"threshold_uncertainty_score":0.03357369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007752262576708207,"score_gpt":0.2278854851219063,"score_spread":0.220133222545198,"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."}}