{"id":"W2728943345","doi":"10.17645/up.v2i2.937","title":"Investigating the Potential of Ridesharing to Reduce Vehicle Emissions","year":2017,"lang":"en","type":"article","venue":"Urban Planning","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; George Cedric Metcalf Charitable Foundation","keywords":"Transport engineering; Greenhouse gas; Traffic congestion; TRIPS architecture; Fuel efficiency; Population; Public transport; Environmental economics; Business; Engineering; Automotive engineering; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005516131,0.0003081473,0.0001800957,0.0006878662,0.0002862762,0.0007355385,0.0004995331,0.000400736,0.001520726],"category_scores_gemma":[0.001098052,0.0001054957,0.0004575241,0.0009198104,0.0002138258,0.0008335642,0.0003004209,0.0002131939,0.0002077781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007670094,"about_ca_system_score_gemma":0.0008801097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02379463,"about_ca_topic_score_gemma":0.02738781,"domain_scores_codex":[0.9997452,0.00006233309,0.00001263181,0.00004183493,0.00007790032,0.00006005072],"domain_scores_gemma":[0.9996147,0.0001459288,0.00005546274,0.00002798433,0.0001369386,0.00001899153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009450304,0.000944352,0.3898041,0.002179616,0.0005833446,0.00153521,0.001212447,0.1723783,0.04342844,0.02135561,0.003899201,0.3617344],"study_design_scores_gemma":[0.000119311,0.006206166,0.5641999,0.0005287034,0.001169713,0.000747128,0.01185442,0.2521737,0.075072,0.01773363,0.07002362,0.0001716989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784065,0.001461261,0.002783539,0.0004430523,0.00002062651,0.00007085088,0.0003643217,0.00004411087,0.01640571],"genre_scores_gemma":[0.9954603,0.001149988,0.001781482,0.00003616392,0.000007956388,0.00002250408,0.0001736168,0.0000040512,0.001364028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02379463,"threshold_uncertainty_score":0.04731226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03169048844878859,"score_gpt":0.2768858287099361,"score_spread":0.2451953402611475,"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."}}