{"id":"W3158286244","doi":"10.3386/w28516","title":"Measuring Commuting and Economic Activity inside Cities with Cell Phone Records","year":2021,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Tokyo; International Development Research Centre; Asian Development Bank; Fetzer Institute","keywords":"Phone; Wage; Transaction data; Simple (philosophy); Predictive power; Database transaction; Distribution (mathematics); Econometrics; Power (physics); Computer science; Economics; Demographic economics; Labour economics; Mathematics; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005012063,0.0003550899,0.0002354447,0.00134529,0.0003173508,0.0006676291,0.0004373778,0.0003628091,0.0027343],"category_scores_gemma":[0.003663712,0.0002620464,0.000376786,0.002634098,0.000210524,0.0005975748,0.0007025359,0.0003745334,0.001003912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004891779,"about_ca_system_score_gemma":0.0005698683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1254171,"about_ca_topic_score_gemma":0.2339216,"domain_scores_codex":[0.9995628,0.0001627831,0.00003129991,0.00009123246,0.0001033236,0.00004849063],"domain_scores_gemma":[0.9987214,0.0005545068,0.000210562,0.000240699,0.0002029889,0.0000697412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003998973,0.0001102887,0.9520028,0.00004956487,0.0001288199,0.00004407062,0.0002245229,0.01909734,0.001153264,0.0006025165,0.001607318,0.02493952],"study_design_scores_gemma":[0.000008306054,0.00005141732,0.9107819,0.0000298861,0.00005872748,0.00005509108,0.000530659,0.08305222,0.001815345,0.0008129837,0.002772492,0.0000309534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623947,0.0001064979,0.01245654,0.0003241135,0.00001304825,0.00006826742,0.01941598,0.0001710405,0.005049891],"genre_scores_gemma":[0.9761825,0.000111091,0.01291961,0.00002458323,0.000009982855,0.00005336478,0.009433489,0.00001303963,0.001252316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1254171,"threshold_uncertainty_score":0.2493742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4072197383336385,"score_gpt":0.4794044995150171,"score_spread":0.07218476118137862,"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."}}