{"id":"W1968319261","doi":"10.1017/s0021932011000411","title":"MONTH OF BIRTH, SOCIOECONOMIC BACKGROUND AND HEIGHT IN RURAL CHINESE MEN","year":2011,"lang":"en","type":"article","venue":"Journal of Biosocial Science","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Socioeconomic status; Demography; Social class; Geography; Rural area; Medicine; Population; Sociology; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.002160501,0.00008050626,0.0002356222,0.0002837393,0.0002819775,0.00005642087,0.000486695,0.00006444005,0.0001636402],"category_scores_gemma":[0.00006518275,0.00006031687,0.00007767424,0.0005888446,0.001782119,0.0007446925,0.00005645589,0.0001276659,0.00000190219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006349835,"about_ca_system_score_gemma":0.0004436549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002696154,"about_ca_topic_score_gemma":0.005386099,"domain_scores_codex":[0.9987512,0.00009576393,0.0003729357,0.000102311,0.0004036822,0.0002740543],"domain_scores_gemma":[0.9992124,0.00005545989,0.0003908669,0.00006104208,0.0001210451,0.0001591475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006336246,0.000144668,0.8192172,0.000008208479,0.00002354079,0.00000646225,0.1220997,4.267233e-7,0.001292927,0.01862063,0.000138091,0.03838472],"study_design_scores_gemma":[0.000345091,0.0001256019,0.9377477,0.00001826688,0.00000680037,0.000003278741,0.03533617,0.000006261906,0.00009894336,0.02596781,0.0002454812,0.00009857315],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9654879,0.0003483306,0.000005041885,0.0004981017,0.0002986348,0.00004699375,0.000002565526,0.000004007592,0.03330842],"genre_scores_gemma":[0.9988486,0.0005929272,0.0002975177,0.00002648901,0.0001393448,5.298395e-7,5.204544e-8,0.000002569891,0.00009194264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1185305,"threshold_uncertainty_score":0.6566291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03893905002683967,"score_gpt":0.3103042927758553,"score_spread":0.2713652427490156,"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."}}