{"id":"W4390100484","doi":"10.1145/3589132.3625637","title":"One-Shot Traffic Assignment with Forward-Looking Penalization","year":2023,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"CHIST-ERA; Horizon 2020 Framework Programme; European Commission","keywords":"Metis; Baseline (sea); Computer science; Shot (pellet); Enhanced Data Rates for GSM Evolution; TRIPS architecture; Transport engineering; Operations research; Artificial intelligence; Engineering; Parallel computing; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001020493,0.001800771,0.002279214,0.001029005,0.001153368,0.001383452,0.003193775,0.002267027,0.005012376],"category_scores_gemma":[0.003820409,0.0008658246,0.001188993,0.001065733,0.001054626,0.001888604,0.001980799,0.001912862,0.0009238964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009098185,"about_ca_system_score_gemma":0.002167026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01095975,"about_ca_topic_score_gemma":0.01642275,"domain_scores_codex":[0.9990904,0.0002380969,0.00002818676,0.0003028987,0.0001905686,0.0001498341],"domain_scores_gemma":[0.9985159,0.0008323191,0.00008781409,0.0001846067,0.0002389041,0.0001404301],"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":[0.0002419743,0.0003547537,0.001187937,0.0001338615,0.0001188141,0.0001735627,0.0001434126,0.8371969,0.004730638,0.008279361,0.006362252,0.1410766],"study_design_scores_gemma":[0.00001039437,0.00002820243,0.00009042213,0.000003886681,0.00000799462,0.00002095159,0.0000170514,0.9960372,0.0004380705,0.00296553,0.0003741836,0.000006096644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03314229,0.0001784251,0.9613683,0.0001927643,0.0001012271,0.0001195519,0.0001088031,0.00197462,0.002814164],"genre_scores_gemma":[0.6021424,0.0001056575,0.3891636,0.0004191393,0.0001065776,0.000282242,0.00100826,0.0005549938,0.006217118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01095975,"threshold_uncertainty_score":0.02179188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05207799865951866,"score_gpt":0.3098555889715817,"score_spread":0.257777590312063,"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."}}