{"id":"W2098818981","doi":"10.3141/2405-08","title":"Use of Subway Smart Card Transactions for the Discovery and Partial Correction of Travel Survey Bias","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"","keywords":"Smart card; TRIPS architecture; Metropolitan area; Respondent; Transit (satellite); Public transport; Population; Occupancy; Survey data collection; Travel behavior; Geography; Transport engineering; Data collection; Business; Demographic economics; Statistics; Computer science; Economics; Engineering; Medicine; Computer security; Environmental health; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.101081,0.001366508,0.001774407,0.005415269,0.001688183,0.002373417,0.003540858,0.001086005,0.00558918],"category_scores_gemma":[0.331042,0.0009288976,0.002708735,0.01131984,0.001685704,0.002211401,0.003543381,0.001317772,0.0009945163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001450441,"about_ca_system_score_gemma":0.006996219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01988469,"about_ca_topic_score_gemma":0.02231791,"domain_scores_codex":[0.8666772,0.09877823,0.01133075,0.01003447,0.01123661,0.001942786],"domain_scores_gemma":[0.6405446,0.1796146,0.04268456,0.1037707,0.03164912,0.001736411],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009296464,0.0001448909,0.5186057,0.001241853,0.002313351,0.0006553985,0.005632214,0.007695245,0.002891136,0.01299925,0.008377289,0.438514],"study_design_scores_gemma":[0.0003777434,0.002341118,0.650493,0.0009808198,0.003740878,0.001905764,0.00482703,0.1810187,0.02792921,0.02613918,0.09974185,0.0005045761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.209068,0.000987908,0.767799,0.0008435191,0.0006469631,0.004651436,0.00533682,0.002960241,0.007706077],"genre_scores_gemma":[0.6541248,0.000344625,0.3347111,0.000265016,0.0001433247,0.003649277,0.002214753,0.0005283633,0.004018706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.898919,"threshold_uncertainty_score":0.5345736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2206174164610597,"score_gpt":0.4121579017486646,"score_spread":0.1915404852876049,"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."}}