{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01172886,0.00008910342,0.0002260377,0.0004497544,0.0009477759,0.0001020427,0.0001649593,0.00009987098,0.0001855052],"category_scores_gemma":[0.001489361,0.00007361068,0.0001052875,0.003364508,0.001686999,0.0005125505,0.000003615625,0.0002176203,0.000002773933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001718018,"about_ca_system_score_gemma":0.0002807865,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1499654,"about_ca_topic_score_gemma":0.05454814,"domain_scores_codex":[0.9956487,0.002567414,0.0005042474,0.0002749714,0.0007239495,0.0002807058],"domain_scores_gemma":[0.9969789,0.001533788,0.0002155667,0.0002247048,0.0009112374,0.0001357468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00623277,0.001343409,0.05282146,0.0004846423,0.005119273,0.00001653324,0.4392172,0.0005717147,0.002937727,0.4731852,0.001975319,0.01609479],"study_design_scores_gemma":[0.00556808,0.0002115049,0.6281438,0.0000629362,0.01124702,0.000002410707,0.1142766,0.003233597,0.002077995,0.03138963,0.2028455,0.0009409885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8568992,0.001833379,0.05115252,0.07592043,0.00008897371,0.002132983,0.0003315819,0.0001038433,0.01153707],"genre_scores_gemma":[0.9970526,0.0020403,0.00004954043,0.0001326049,0.00006337654,0.00006888657,0.000104731,0.000005595699,0.0004823515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5753223,"threshold_uncertainty_score":0.9627039,"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."}}