{"id":"W650771425","doi":"10.5038/2375-0901.3.3.1","title":"Factor Analysis for the Study of Determinants of Public Transit Ridership","year":2001,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Logistic regression; Public transport; Uncorrelated; Factor (programming language); Odds; Transit (satellite); Factor analysis; Econometrics; Statistics; Transport engineering; Business; Computer science; Engineering; Mathematics","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":[0.03064428,0.00287882,0.002569839,0.005626953,0.002117956,0.001994084,0.00112341,0.0008197904,0.01055522],"category_scores_gemma":[0.08138498,0.0009590792,0.003990678,0.009259247,0.001210246,0.002402619,0.001803469,0.002592637,0.00180028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001442797,"about_ca_system_score_gemma":0.004736048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008280893,"about_ca_topic_score_gemma":0.007103673,"domain_scores_codex":[0.9724692,0.02127027,0.001574408,0.001212984,0.003090448,0.0003827674],"domain_scores_gemma":[0.9452354,0.04447519,0.002363958,0.003729911,0.003670754,0.0005247055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001408904,0.00122931,0.0914438,0.002157916,0.003102015,0.0006126759,0.005390484,0.0127698,0.0057852,0.06781126,0.02772096,0.7805676],"study_design_scores_gemma":[0.001401509,0.007241736,0.2948919,0.003789567,0.001923285,0.00156446,0.01053089,0.3954414,0.004777295,0.1799739,0.09760193,0.000862035],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06214246,0.002276567,0.921885,0.001106216,0.000623949,0.003203342,0.002726282,0.001776326,0.00425982],"genre_scores_gemma":[0.2048995,0.0015635,0.7780768,0.0001298064,0.0002506981,0.00907395,0.003205574,0.0003083243,0.002491899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03064428,"threshold_uncertainty_score":0.1620644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1407346580539166,"score_gpt":0.3622694168378593,"score_spread":0.2215347587839427,"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."}}