{"id":"W4403707575","doi":"10.2139/ssrn.4959493","title":"The Marginal Benefit of Bicycles in Education Programs: An Evaluation Using Optimal Full Matching","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Queen's University","funders":"","keywords":"Matching (statistics); Computer science; Mathematical optimization; Mathematics; Statistics","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":[0.02033316,0.001757283,0.005653632,0.002102396,0.0008431005,0.001917885,0.003309049,0.003443656,0.02357326],"category_scores_gemma":[0.0476613,0.0009862337,0.003936469,0.002554478,0.002029991,0.003480939,0.002545087,0.001737374,0.0008435787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003068104,"about_ca_system_score_gemma":0.004196416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0101979,"about_ca_topic_score_gemma":0.003795849,"domain_scores_codex":[0.9759897,0.01962666,0.0005627351,0.001185792,0.001272822,0.001362202],"domain_scores_gemma":[0.9590278,0.03566385,0.001263451,0.002028583,0.0007283211,0.001287987],"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":[0.3187997,0.01730609,0.01393178,0.005934863,0.005450558,0.0001714338,0.0003990424,0.3376016,0.0007964968,0.02130792,0.003021378,0.2752792],"study_design_scores_gemma":[0.1072771,0.1120464,0.03660181,0.001529886,0.02190271,0.0003606486,0.001953683,0.6374819,0.003423673,0.07139652,0.00570327,0.0003223078],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9378263,0.007392283,0.03579981,0.001402011,0.0003975219,0.00303323,0.001778623,0.0003376309,0.01203258],"genre_scores_gemma":[0.9932853,0.0005672742,0.004246332,0.0001156678,0.00004982686,0.0004623535,0.0001878569,0.00002339573,0.001062039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02357326,"threshold_uncertainty_score":0.1075333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03352179141495897,"score_gpt":0.366100639620089,"score_spread":0.33257884820513,"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."}}