{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002779708,0.00004533826,0.00005624615,0.00007758687,0.0003273406,0.00005949151,0.00005177375,0.00003851747,0.00029674],"category_scores_gemma":[0.0000170614,0.00004196435,0.00001613841,0.0005250284,0.00003724728,0.0001620641,0.000001763715,0.00003137874,0.00005685981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003059368,"about_ca_system_score_gemma":0.00007110995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001284169,"about_ca_topic_score_gemma":0.001184902,"domain_scores_codex":[0.9992493,0.00003772363,0.00009687634,0.0001212206,0.0003371454,0.0001577029],"domain_scores_gemma":[0.9997726,0.00003967796,0.00003848291,0.00004277501,0.00005235693,0.00005408867],"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":[0.0000449803,0.00008380754,0.02437611,0.00002336738,0.00004702615,0.00000969574,0.0447974,0.8663895,0.00007454444,0.0402559,0.004492661,0.01940502],"study_design_scores_gemma":[0.005109617,0.0007087031,0.478667,0.0005656796,0.000368566,0.000002127181,0.1217264,0.1776072,0.001497187,0.001774125,0.2095923,0.002381125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5211935,0.00002405593,0.2851971,0.004687209,0.0003506893,0.0006876146,0.000008984097,0.00232319,0.1855276],"genre_scores_gemma":[0.9928972,0.00003370388,0.001083484,0.0001048686,0.0000485333,0.00001231962,0.0001003153,0.000007902036,0.005711658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6887823,"threshold_uncertainty_score":0.3249095,"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."}}