{"id":"W2254408903","doi":"10.1088/1748-9326/10/12/124017","title":"Characterizing the GHG emission impacts of carsharing: a case of Vancouver","year":2015,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Transport Canada; National Science Foundation","keywords":"Greenhouse gas; TRIPS architecture; Train; Public transport; Transport engineering; Business; Car sharing; Service (business); Environmental economics; Economics; Marketing; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002736585,0.0006295794,0.0003945589,0.0006121303,0.001871794,0.002163525,0.00121655,0.001483321,0.002405137],"category_scores_gemma":[0.0008067637,0.0003923119,0.0006727137,0.001486666,0.0009198419,0.0007079573,0.0008985355,0.0009393364,0.0001853353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009186187,"about_ca_system_score_gemma":0.002822845,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8190868,"about_ca_topic_score_gemma":0.8142342,"domain_scores_codex":[0.9997723,0.00004610085,0.000005653878,0.00003425258,0.00004487772,0.0000968834],"domain_scores_gemma":[0.9996063,0.0001775968,0.00002195563,0.00002532462,0.0001148973,0.0000539883],"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":[0.0002876287,0.0005144922,0.07761743,0.0001295757,0.00009409489,0.005849465,0.0006873256,0.8866855,0.005966323,0.009831575,0.001391372,0.01094519],"study_design_scores_gemma":[0.00009202887,0.0001503126,0.02927206,0.0000250838,0.00006242978,0.0002618585,0.004165576,0.9569471,0.002840264,0.003069942,0.003047725,0.00006561357],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901707,0.00007556535,0.001389692,0.0001721115,0.000004452469,0.00005050529,0.0003178245,0.00001780823,0.007801369],"genre_scores_gemma":[0.9957075,0.0001009054,0.0008164152,0.0000180681,0.000001476679,0.00001769246,0.0001641885,0.000007935545,0.003165913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1809132,"threshold_uncertainty_score":0.3639572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04658413952474841,"score_gpt":0.3000343622897437,"score_spread":0.2534502227649953,"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."}}