{"id":"W2026634277","doi":"10.1061/(asce)0733-947x(2007)133:10(549)","title":"G-EMME/2: Automatic Calibration Tool of the EMME/2 Transit Assignment Using Genetic Algorithms","year":2007,"lang":"en","type":"article","venue":"Journal of Transportation Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Set (abstract data type); Calibration; Process (computing); Genetic algorithm; Transit (satellite); Computer science; Algorithm; Software; Mathematical optimization; Engineering; Public transport; Machine learning; Transport engineering; Mathematics; Programming language; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001568593,0.001077351,0.0006273226,0.001608303,0.0005339679,0.0008671457,0.001118405,0.0008334483,0.00514276],"category_scores_gemma":[0.007069403,0.0006354568,0.0005787925,0.0008392472,0.0003337373,0.0009466269,0.0008172792,0.001034814,0.0009871564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007659898,"about_ca_system_score_gemma":0.001085412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00585564,"about_ca_topic_score_gemma":0.005735481,"domain_scores_codex":[0.9992824,0.0002487862,0.00002777647,0.0001495769,0.0002414446,0.00005006134],"domain_scores_gemma":[0.9982827,0.0009303237,0.0001784706,0.0002561769,0.0003281196,0.00002428414],"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":[0.000225056,0.0001824667,0.003993812,0.0000981562,0.00007818982,0.0001110112,0.0002552194,0.620249,0.01021902,0.009645238,0.007524664,0.3474182],"study_design_scores_gemma":[0.00002452876,0.00002677059,0.0007168124,0.0000122432,0.00000805653,0.00002928951,0.00002640027,0.9864166,0.007134134,0.002134866,0.003452166,0.00001808599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0185491,0.00001931961,0.9695268,0.00005267371,0.00001707573,0.00005954658,0.0001702773,0.009649492,0.001955717],"genre_scores_gemma":[0.1844326,0.00003116113,0.8123198,0.00005670932,0.000008123982,0.0002120695,0.000489558,0.001066516,0.001383475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00585564,"threshold_uncertainty_score":0.01720423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486237434063044,"score_gpt":0.2564245515283288,"score_spread":0.2415621771876984,"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."}}