{"id":"W2040395924","doi":"10.3141/2196-03","title":"Multiobjective Optimization for Multimodal Evacuation","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Traffic congestion; Transit (satellite); Public transport; Vehicle routing problem; Transport engineering; Scheduling (production processes); Budget constraint; Flow network; Operations research; Routing (electronic design automation); Engineering; Mathematical optimization; Computer network; Operations management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003292941,0.0002694558,0.0003725007,0.001134134,0.0006649267,0.0001317343,0.0007587295,0.0003056379,0.000378485],"category_scores_gemma":[0.0003135229,0.0002272803,0.0003897758,0.002164118,0.0004746553,0.0008691278,0.000002461642,0.002424731,0.00001248229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001879694,"about_ca_system_score_gemma":0.0004273242,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001157118,"about_ca_topic_score_gemma":0.03651186,"domain_scores_codex":[0.9950727,0.0002752515,0.001469301,0.0003444976,0.002101011,0.0007372267],"domain_scores_gemma":[0.9915572,0.001007421,0.0002698098,0.0004643625,0.006415047,0.0002861559],"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":[0.001805409,0.0008037281,0.1024414,0.0008194398,0.0005864782,0.00002415422,0.009402284,0.7179953,0.1041741,0.02229257,0.01182352,0.02783162],"study_design_scores_gemma":[0.003956088,0.0004658469,0.8997197,0.0001691749,0.0001174831,7.18909e-7,0.00196545,0.05736418,0.01176677,0.003420156,0.0206129,0.0004415346],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9077569,0.000045531,0.08660886,0.001231125,0.001822861,0.002009984,0.0002290906,0.0001165285,0.0001790989],"genre_scores_gemma":[0.9744168,0.0001771584,0.02436171,0.00003228459,0.000312945,0.0003259876,0.0001135643,0.00008629361,0.0001732453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7972783,"threshold_uncertainty_score":0.9998767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06105033205017096,"score_gpt":0.3762983787042979,"score_spread":0.3152480466541269,"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."}}