{"id":"W3121333469","doi":"","title":"Estimating crowding costs in public transport","year":2013,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Contingent valuation; Public transport; Externality; Crowding; Willingness to pay; Crowding out; Economics; Valuation (finance); Microeconomics; Traffic congestion; Welfare; Public economics; Public good; Business; Transport engineering; Monetary economics; Finance; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001227558,0.0006077923,0.0004332988,0.001919933,0.0003337087,0.001090349,0.0005203918,0.0007363233,0.001715147],"category_scores_gemma":[0.006483427,0.0003849473,0.0006439602,0.001603049,0.0005113381,0.0008421635,0.0007796417,0.0004894336,0.0001217352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002423916,"about_ca_system_score_gemma":0.0005488466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03240277,"about_ca_topic_score_gemma":0.01532333,"domain_scores_codex":[0.999374,0.0002939935,0.00002231002,0.00008928851,0.00011336,0.0001070625],"domain_scores_gemma":[0.9957289,0.003277196,0.0004423682,0.0002370626,0.0001878112,0.0001265802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002237216,0.0001810054,0.09472832,0.00006845859,0.0001204248,0.0001641769,0.0002875702,0.8677664,0.00103975,0.01500844,0.0006473872,0.01976426],"study_design_scores_gemma":[0.00001946481,0.0001409118,0.1034092,0.00002845522,0.00004450518,0.00009316955,0.0006314089,0.8807248,0.0009146605,0.01302177,0.0009293028,0.00004227561],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868348,0.0001053257,0.01060202,0.00007100288,0.000005212255,0.00003204263,0.0002989116,0.00002614857,0.002024462],"genre_scores_gemma":[0.9965099,0.00004324739,0.002890731,0.000004150686,0.000004511177,0.0000188772,0.0001608571,0.000004667374,0.0003631227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03240277,"threshold_uncertainty_score":0.06442833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1398951977281374,"score_gpt":0.293952374674948,"score_spread":0.1540571769468106,"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."}}