{"id":"W2912586992","doi":"10.3968/10723","title":"The Implementation and Welfare Effect of Vehicle Quantity Regulation Policy: A Case Study of Beijing Vehicle Quota System","year":2018,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Beijing; License; Welfare; Control (management); Business; Traffic congestion; Public transport; Public policy; Government (linguistics); Transport engineering; Intervention (counseling); Public economics; Policy analysis; Economics; Economic growth; Engineering; Public administration; Computer science; Market economy; China; Political science","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":[0.000412645,0.00008977184,0.0001262843,0.00004985504,0.0006127131,0.00007621562,0.0001341033,0.00004072501,0.000003836967],"category_scores_gemma":[0.00003229845,0.00006769279,0.00002655138,0.0003382308,0.000210794,0.0003424318,0.00002775302,0.00007948894,9.14446e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006681861,"about_ca_system_score_gemma":0.000008459081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001934985,"about_ca_topic_score_gemma":0.004574709,"domain_scores_codex":[0.9991656,0.0001086331,0.000422748,0.0000883647,0.0001194441,0.00009520977],"domain_scores_gemma":[0.9989048,0.00009701876,0.0001404536,0.000435622,0.0004004862,0.00002158689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002133747,0.0001854279,0.7360604,0.0009511724,0.0003249408,0.000003182216,0.0413706,0.002745415,0.1179611,0.04343139,0.00008304631,0.05666986],"study_design_scores_gemma":[0.001904649,0.0004567087,0.9246385,0.00005511516,0.00007924793,0.00003017881,0.04267939,0.008991648,0.02062139,0.00002712781,0.0003570868,0.0001590433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989631,0.00008109154,0.00004781405,0.00009319968,0.00004435468,0.0005236011,0.0000181448,0.00009970342,0.0001289446],"genre_scores_gemma":[0.999811,0.00001179187,0.0000510958,0.000002145724,0.00001813314,0.00004392346,0.0000481192,0.000009235229,0.000004558438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.188578,"threshold_uncertainty_score":0.4712556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736165990877141,"score_gpt":0.3540928601859925,"score_spread":0.3367312002772211,"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."}}