{"id":"W1899085136","doi":"10.1002/atr.1226","title":"Designing Emission Charging Schemes for Transportation Conformity","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; China Scholarship Council; University of Florida","keywords":"Conformity; Complementarity (molecular biology); Toll; Scheme (mathematics); Mathematical optimization; Computer science; Transport engineering; Operations research; Simulation; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004922746,0.0001232281,0.0002191509,0.0001627611,0.0003597897,0.00005240377,0.0001198399,0.0001087764,0.00009242475],"category_scores_gemma":[0.00008885497,0.0001200468,0.0001482414,0.0002411841,0.00005554001,0.001771273,1.549799e-7,0.0001394247,0.000002524424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005672762,"about_ca_system_score_gemma":0.0001782213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005667415,"about_ca_topic_score_gemma":0.0001318244,"domain_scores_codex":[0.9984926,0.00003593572,0.0006951695,0.0001336466,0.0004099556,0.0002326965],"domain_scores_gemma":[0.9980927,0.0001550308,0.0006698713,0.00005777367,0.0008734033,0.0001512904],"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":[0.001579617,0.0004914873,0.08497682,0.0005956644,0.0002901711,0.00002906925,0.2609972,0.3068136,0.1226974,0.03053222,0.002617722,0.188379],"study_design_scores_gemma":[0.01200028,0.001042881,0.652041,0.001463092,0.0006533715,0.000005102799,0.0930336,0.001182187,0.06878809,0.01400259,0.1542314,0.001556412],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.64051,0.0001426772,0.3574689,0.0006406943,0.0004673553,0.0005358456,0.00002343866,0.00006054353,0.0001505177],"genre_scores_gemma":[0.9212974,0.0001749966,0.07787598,0.00007599745,0.0001646116,0.00002787947,0.0001934657,0.0000175397,0.0001721516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5670642,"threshold_uncertainty_score":0.4895367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01753643866791943,"score_gpt":0.2951808611829633,"score_spread":0.2776444225150438,"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."}}