{"id":"W1970075137","doi":"10.1016/j.tra.2011.04.002","title":"Geodemographic analysis and the identification of potential business partnerships enabled by transit smart cards","year":2011,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Université du Québec à Montréal; McMaster University","funders":"","keywords":"Smart card; Identification (biology); Payment; Transit (satellite); Business; Service (business); Exploit; Smart city; Payment card; Computer science; Transport engineering; Telecommunications; Marketing; Public transport; Computer security; Engineering; Finance; Internet of Things","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001030602,0.0001602942,0.000151017,0.004810951,0.000582566,0.002255342,0.0004121945,0.0005203323,0.006366131],"category_scores_gemma":[0.009317373,0.0001581784,0.0002168453,0.0071571,0.0007029521,0.003251077,0.001606945,0.0003408219,0.0004769714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009894553,"about_ca_system_score_gemma":0.001038902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006050732,"about_ca_topic_score_gemma":0.01050737,"domain_scores_codex":[0.9991549,0.0005209671,0.00004598332,0.00007094994,0.0001138934,0.00009335021],"domain_scores_gemma":[0.9947837,0.003123635,0.00100295,0.0003809473,0.0005146496,0.0001941424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004500888,0.000203018,0.6788511,0.0002672997,0.00009205531,0.0006050466,0.00945955,0.01169453,0.001448608,0.1505142,0.003778447,0.1426361],"study_design_scores_gemma":[0.00006376743,0.0002777488,0.51332,0.0003861522,0.0001914373,0.001165037,0.1669266,0.1500294,0.004505396,0.1144448,0.04859976,0.000089918],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9651469,0.0002196717,0.00906886,0.000801156,0.00001478362,0.00007386266,0.001291485,0.00003136919,0.02335206],"genre_scores_gemma":[0.9956614,0.00009530985,0.003179594,0.00000871134,0.000002837533,0.00002043337,0.0002194851,0.000001951406,0.0008101616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006366131,"threshold_uncertainty_score":0.02129686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1113399643075145,"score_gpt":0.4010166367840205,"score_spread":0.289676672476506,"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."}}