{"id":"W4381543994","doi":"10.1155/2023/6648740","title":"Road Rescue Demand Prediction for the Improvement of Traffic System Resilience","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Resilience (materials science); Beijing; Transport engineering; Emergency rescue; Towing; Crash; Computer science; Engineering; Geography; Automotive 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":[0.0003397146,0.0009200186,0.0005847268,0.0007579203,0.0003465308,0.0006408591,0.000642533,0.0005227958,0.001559895],"category_scores_gemma":[0.001456138,0.0003114633,0.0006547668,0.0006307795,0.0002040196,0.001073271,0.0005814171,0.0007109549,0.0002963439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006958502,"about_ca_system_score_gemma":0.0008271296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02679283,"about_ca_topic_score_gemma":0.0175989,"domain_scores_codex":[0.9997781,0.00004031417,0.00001380989,0.00008339949,0.00003999838,0.00004437325],"domain_scores_gemma":[0.9995773,0.0001528259,0.00006610841,0.00003822684,0.0001269679,0.00003844584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005105065,0.0000621902,0.01625493,0.00004135561,0.0000343367,0.00007660915,0.00004817176,0.9606284,0.001141265,0.0007202994,0.001186705,0.01975469],"study_design_scores_gemma":[0.000001369276,0.000008320253,0.001907582,0.000002131364,0.000004990964,0.000004727221,0.00002098961,0.9975061,0.0001444242,0.0002768525,0.0001186303,0.000003908657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6838363,0.000345073,0.3072282,0.0007376702,0.0001302201,0.00008546134,0.001699946,0.001147989,0.004789135],"genre_scores_gemma":[0.9943719,0.00006494486,0.004296225,0.00001598436,0.00001281433,0.00002411461,0.0005094743,0.00001913023,0.0006854408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02679283,"threshold_uncertainty_score":0.05327374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360322388219285,"score_gpt":0.2363326535541191,"score_spread":0.2227294296719263,"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."}}