{"id":"W6969156291","doi":"10.5281/zenodo.7459308","title":"Creating a Cohort for Analysis using Indiana's Temporary Assistance for Needy Families Data","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Historical Economic and Social Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workforce; Quarter (Canadian coin); Cohort; Population; Analytics; Workforce development; Cohort study","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007979822,0.0004037545,0.0003433466,0.003998722,0.001805678,0.002222529,0.001306972,0.0003686504,0.07151054],"category_scores_gemma":[0.01882422,0.0005893708,0.0005615721,0.002432688,0.0003186514,0.001183833,0.002736704,0.001382358,0.01527134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001592163,"about_ca_system_score_gemma":0.008224051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06783707,"about_ca_topic_score_gemma":0.1002347,"domain_scores_codex":[0.9979138,0.000593236,0.0002091037,0.000307647,0.000779112,0.0001971203],"domain_scores_gemma":[0.9904959,0.002316345,0.0008578601,0.002448321,0.002926328,0.0009552537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001492042,0.0001867908,0.04402705,0.0001715788,0.00004724771,0.0002873214,0.001884765,0.0007101206,0.0005627363,0.01487518,0.8272502,0.1098478],"study_design_scores_gemma":[0.00009146336,0.00007281089,0.04609638,0.0004816654,0.00002391938,0.0001812277,0.002510297,0.001453431,0.001295822,0.003285108,0.9444543,0.00005373552],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03600516,0.0002090442,0.08009465,0.004099265,0.001011991,0.01703017,0.7742957,0.004053136,0.08320093],"genre_scores_gemma":[0.0773744,0.0006465123,0.3394025,0.00186303,0.0003775819,0.04015414,0.4154858,0.00264824,0.1220478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07151054,"threshold_uncertainty_score":0.2392266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1079619662251281,"score_gpt":0.2654550102118708,"score_spread":0.1574930439867427,"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."}}