{"id":"W2734914701","doi":"10.1016/j.ehb.2017.07.001","title":"Body mass and wages: New evidence from quantile estimation","year":2017,"lang":"en","type":"article","venue":"Economics & Human Biology","topic":"Economics of Agriculture and Food Markets","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Quantile; Quantile regression; Wage; Economics; Standard deviation; Socioeconomic status; Body mass index; Distribution (mathematics); Estimation; Statistical discrimination; Percentage point; Demographic economics; Demography; Econometrics; Labour economics; Statistics; Medicine; Mathematics; Population; Endocrinology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003969365,0.0002387038,0.0005662008,0.0001111364,0.0004960244,0.0003947562,0.0006431248,0.0002581558,0.0004626159],"category_scores_gemma":[0.0001667419,0.0002691465,0.00009907266,0.00001424251,0.0001777185,0.0007900872,0.000210109,0.0001740392,0.000667117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008304937,"about_ca_system_score_gemma":0.00002293552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001924528,"about_ca_topic_score_gemma":0.0005301864,"domain_scores_codex":[0.9982432,0.00001444137,0.0006625132,0.0007466106,0.000006774434,0.0003264256],"domain_scores_gemma":[0.9980488,0.0001249512,0.0008551365,0.0008012375,0.00001462678,0.0001552492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005275706,0.00004756521,0.3561256,0.00001890063,0.0002462022,0.000002942675,0.0004289998,0.00004002082,0.001436939,0.6310455,0.005696045,0.004858561],"study_design_scores_gemma":[0.0007356532,0.0001708632,0.4095021,0.00002854879,0.00001670205,0.000003388462,0.00002599914,0.001428664,0.000364281,0.5674796,0.01971922,0.0005250113],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9766629,0.002803027,0.0008095179,0.001916506,0.0007334619,0.000198371,0.0001630167,0.000041813,0.01667132],"genre_scores_gemma":[0.9935228,0.001960413,0.002586327,0.0002300648,0.000478363,0.00001386959,0.00009159779,0.00002374872,0.001092771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06356592,"threshold_uncertainty_score":0.9999761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05963492074762415,"score_gpt":0.2693455549483738,"score_spread":0.2097106342007496,"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."}}