{"id":"W2072158892","doi":"10.2139/ssrn.519462","title":"Training and Lifetime Income","year":2005,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Training (meteorology); Economics; Demographic economics; Geography","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":[0.0007133431,0.0001771065,0.0002867649,0.000782994,0.0007682333,0.0009771325,0.0005494899,0.001096535,0.01589593],"category_scores_gemma":[0.005815244,0.0001626185,0.000374832,0.0008159505,0.0003323342,0.0008700246,0.0006712075,0.001425863,0.001886289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005452969,"about_ca_system_score_gemma":0.0005188579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01367018,"about_ca_topic_score_gemma":0.01395538,"domain_scores_codex":[0.9996392,0.00008147308,0.0000209692,0.0000335922,0.00004229953,0.0001824647],"domain_scores_gemma":[0.9939023,0.001369994,0.001551814,0.0002257416,0.0003835825,0.002566673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006744641,0.0008152743,0.9791083,0.00003077089,0.00007730137,0.0001572003,0.0003300553,0.000653273,0.0001528187,0.001183663,0.002181951,0.0146349],"study_design_scores_gemma":[0.00001577178,0.0002037972,0.9956228,0.00003354708,0.00004142369,0.0001424379,0.0005108326,0.0009391832,0.00007493555,0.0007559803,0.001651249,0.000007969984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990104,0.001171515,0.0001232619,0.00176191,0.00005524481,0.00000895836,0.001644223,0.0000156072,0.00511524],"genre_scores_gemma":[0.9928043,0.0003122324,0.00003251083,0.0000709806,0.00004652261,0.00000639901,0.0006360571,0.000003438427,0.006087492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01589593,"threshold_uncertainty_score":0.05317718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01627695121034701,"score_gpt":0.2252213230302166,"score_spread":0.2089443718198696,"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."}}