{"id":"W2907856445","doi":"","title":"Are transportation network companies revolutionizing urban mobility? A comparative media analysis on Uber in Paris and Montreal","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Transport engineering; Business; Regional science; Telecommunications; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001461125,0.0003691211,0.0007583008,0.0002581776,0.0002517524,0.0002236256,0.0004904692,0.0002989489,0.00002962537],"category_scores_gemma":[0.000147173,0.0004067225,0.00019201,0.0003646904,0.000288091,0.0001133146,0.00006576678,0.0007377359,0.000005055545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393112,"about_ca_system_score_gemma":0.00004119115,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002567242,"about_ca_topic_score_gemma":0.1009713,"domain_scores_codex":[0.9973865,0.0008303268,0.0005852635,0.0005893566,0.0002785023,0.0003300677],"domain_scores_gemma":[0.9968888,0.0007762979,0.0003888654,0.001283955,0.0005178757,0.0001442548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007010581,0.0007202238,0.5864342,0.0006444342,0.001410922,0.00004655917,0.05551858,0.3238631,0.0000388205,0.02453415,0.003899043,0.002819864],"study_design_scores_gemma":[0.0003550531,2.922573e-7,0.7887534,0.0009760049,0.0003315891,4.400941e-7,0.0001604474,0.2062394,0.0001491436,0.001834287,0.0008077581,0.0003921876],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.831269,0.01014564,0.1291946,0.0008644925,0.0004622693,0.0009358278,0.0008144819,0.0005874534,0.02572619],"genre_scores_gemma":[0.9920604,0.001233002,0.005015898,0.00001417357,0.00003576476,0.00005922206,0.001298604,0.00003139266,0.0002515531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2023192,"threshold_uncertainty_score":0.9998385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03104925680039825,"score_gpt":0.2236268207695295,"score_spread":0.1925775639691313,"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."}}