{"id":"W1536700475","doi":"","title":"Decision-making of highway emergency rescue based on CBR","year":2010,"lang":"en","type":"article","venue":"Chinese Control Conference","topic":"Safety and Risk Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Emergency rescue; Case-based reasoning; Computer science; Process (computing); Matching (statistics); Service (business); Status quo; Function (biology); Operations research; Plan (archaeology); Emergency management; Transport engineering; Engineering; Artificial intelligence; Business; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004976693,0.0002715758,0.0003603054,0.0003207683,0.0001809113,0.00009584291,0.0005908315,0.0001009016,0.002339917],"category_scores_gemma":[0.000921643,0.0001962472,0.0001625487,0.0004608597,0.00006825213,0.0004108572,0.0001187192,0.0002941593,0.0002715382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008383186,"about_ca_system_score_gemma":0.00003586437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001205304,"about_ca_topic_score_gemma":0.0006578949,"domain_scores_codex":[0.9984221,0.00001394903,0.0004716686,0.0003839785,0.000414551,0.0002937783],"domain_scores_gemma":[0.9985444,0.0001749421,0.0002690343,0.0006405207,0.0003541348,0.00001697306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003007512,0.001306696,0.2739614,0.0008130909,0.0002207676,0.00009558687,0.0001730255,0.01233124,0.0163349,0.4364113,0.0110826,0.2442619],"study_design_scores_gemma":[0.003539474,0.0000520901,0.4295962,0.0002951852,0.0001181832,6.993806e-7,0.00004315342,0.492953,0.00003134611,0.03987226,0.03282748,0.0006709238],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8479168,0.00007139669,0.04650278,0.002898322,0.005144559,0.0008332283,0.00001720779,0.0002828827,0.09633286],"genre_scores_gemma":[0.9973572,0.000007423677,0.000333468,0.00131272,0.0007952634,0.00002784871,0.000009009837,0.00002544872,0.0001315839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4806218,"threshold_uncertainty_score":0.9985721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00963315132635251,"score_gpt":0.2483414903008055,"score_spread":0.238708338974453,"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."}}