{"id":"W4391308499","doi":"10.1146/knowable-012924-2","title":"Descifrando la genética de nuestros rasgos faciales","year":2024,"lang":"es","type":"article","venue":"Knowable Magazine","topic":"Aging, Health, and Disability","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001419766,0.0004582943,0.0007334158,0.0001962544,0.0002181992,0.000311747,0.0002299389,0.0003783544,0.004292749],"category_scores_gemma":[0.0007190134,0.0004050948,0.000274618,0.0005457284,0.0005272077,0.0001919743,0.0001144303,0.0007460641,0.006179858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00044503,"about_ca_system_score_gemma":0.001658413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002884486,"about_ca_topic_score_gemma":0.00005584046,"domain_scores_codex":[0.9967077,0.0003076113,0.0006656981,0.0007903601,0.0004559374,0.001072659],"domain_scores_gemma":[0.9977347,0.0007098549,0.00007407126,0.0006956061,0.0001236045,0.0006621521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002082703,0.002837966,0.2743187,0.04006326,0.0009323256,0.002873477,0.009784879,0.00009743444,0.03114345,0.07383711,0.4089791,0.1530497],"study_design_scores_gemma":[0.002407674,0.0003609244,0.04656908,0.001644982,0.0006163992,0.0002198724,0.0003022262,0.005012743,0.001028295,0.003164364,0.9381632,0.0005102407],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4677601,0.07239714,0.006500208,0.009386223,0.002380504,0.001856301,0.0003115469,0.001131413,0.4382765],"genre_scores_gemma":[0.9689712,0.002672018,0.001034242,0.000958778,0.002024062,0.00005948872,0.00007081352,0.0001058945,0.02410354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5291842,"threshold_uncertainty_score":0.9998401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.017691179803248,"score_gpt":0.3160074919475394,"score_spread":0.2983163121442913,"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."}}