{"id":"W2245381200","doi":"","title":"Factors affecting urban transit ridership","year":2000,"lang":"en","type":"article","venue":"Rosa P: A digital library for transportation research (United States Department of Transportation)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Transport engineering; Urban transit; Business; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005317175,0.0003433453,0.0004066491,0.0006717329,0.0009319401,0.0003419916,0.0003926838,0.0002215213,0.00138469],"category_scores_gemma":[0.000037576,0.0003581889,0.0003541936,0.002333441,0.0004545708,0.003287935,8.147554e-7,0.0003082344,0.00001848593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004925528,"about_ca_system_score_gemma":0.0002777318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008641667,"about_ca_topic_score_gemma":0.000682115,"domain_scores_codex":[0.9962141,0.000182497,0.0009169187,0.0005735443,0.001271027,0.0008419483],"domain_scores_gemma":[0.9976766,0.001059895,0.0002091679,0.000225644,0.0004019107,0.0004268412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.006059814,0.003864443,0.3297194,0.00232358,0.001330363,0.00009662089,0.3200937,0.09182719,0.00002638051,0.09251344,0.1317468,0.02039829],"study_design_scores_gemma":[0.002154425,0.0007066954,0.06428818,0.0002188728,0.0001397594,1.145736e-7,0.02286791,0.0005655552,0.00110322,0.002477451,0.904781,0.0006967561],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8571305,0.0001164097,0.003389515,0.0008912548,0.00009073738,0.001494748,0.1358666,0.0004649029,0.000555278],"genre_scores_gemma":[0.6646821,0.0002410657,0.000712533,0.000051421,0.00005225737,0.000100497,0.3324647,0.00006361355,0.001631785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7730343,"threshold_uncertainty_score":0.999887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05701436770222572,"score_gpt":0.3163949314762306,"score_spread":0.2593805637740049,"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."}}