{"id":"W3172192278","doi":"10.1155/2021/5283283","title":"Detecting Invalid Associations between Fare Machines and Metro Stations Using Smart Card Data","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data mining; Data collection; Data quality; Quality (philosophy); Association (psychology); Isolation (microbiology); Volume (thermodynamics); Engineering; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001064918,0.00007088032,0.0002187698,0.0001259119,0.0005945617,0.00006516065,0.0001215896,0.00005228715,0.00002812712],"category_scores_gemma":[0.0008396954,0.00007605382,0.00007373342,0.0005060624,0.0000518713,0.001024337,0.000004253513,0.0001646691,3.958197e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118482,"about_ca_system_score_gemma":0.000342031,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001186092,"about_ca_topic_score_gemma":0.03593789,"domain_scores_codex":[0.9986391,0.000218278,0.000488693,0.0001357189,0.00037619,0.0001420281],"domain_scores_gemma":[0.9983524,0.0004039442,0.0004446059,0.0001323763,0.0005629346,0.0001037396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001166675,0.0000801605,0.9049106,0.00003643985,0.000292166,0.000009957885,0.02893013,0.02367331,0.001733831,0.0004583726,0.00001887966,0.03984451],"study_design_scores_gemma":[0.0004514072,0.00002614665,0.9635036,0.00006462444,0.0006829263,8.367099e-7,0.03148193,0.0003930077,0.0004239103,0.001288146,0.001522223,0.0001612505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719253,0.0003774948,0.02665586,0.0005289558,0.0001683445,0.00006430575,0.0002205066,0.00001156797,0.00004764577],"genre_scores_gemma":[0.9886053,0.0001115709,0.01057647,0.0000371405,0.000291933,8.235461e-7,0.0003511607,0.000007242213,0.00001833044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05859301,"threshold_uncertainty_score":0.9816537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07181426420253174,"score_gpt":0.3701012849389659,"score_spread":0.2982870207364342,"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."}}