{"id":"W7084131201","doi":"10.64628/aam.gs5rjs6ug","title":"Breeding young men for export in poor countries","year":2019,"lang":"en","type":"article","venue":"","topic":"Economic and Technological Innovation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"","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":[0.001357957,0.0001463375,0.000359605,0.0006471487,0.001608681,0.001426776,0.000331697,0.0007550958,0.01113408],"category_scores_gemma":[0.003521812,0.0001770366,0.0001905632,0.0006028618,0.000739208,0.0008319863,0.001063095,0.0009630343,0.001068696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004501637,"about_ca_system_score_gemma":0.001097017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007959603,"about_ca_topic_score_gemma":0.01512151,"domain_scores_codex":[0.9995883,0.0001608634,0.000007369892,0.00002759829,0.00002186459,0.0001940456],"domain_scores_gemma":[0.9968897,0.0006675769,0.0005947827,0.00009308742,0.000158459,0.001596449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006181533,0.00062963,0.8629614,0.0002051752,0.00008489926,0.002182204,0.007774933,0.0007647459,0.001481304,0.01264819,0.02157783,0.08907143],"study_design_scores_gemma":[0.0002081784,0.001152726,0.8909905,0.0007600427,0.0002124278,0.00129428,0.04694382,0.00193089,0.00159883,0.01161346,0.04324615,0.00004871174],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842247,0.001063378,0.0003768123,0.005243851,0.00008604804,0.00002109356,0.0001810611,0.00002281173,0.008780299],"genre_scores_gemma":[0.9902085,0.0006520813,0.0004614308,0.0008098715,0.000039984,0.00001719927,0.00008782034,0.00001019438,0.007712916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01113408,"threshold_uncertainty_score":0.03724724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04127359100925986,"score_gpt":0.2207280903498569,"score_spread":0.179454499340597,"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."}}