{"id":"W2513101422","doi":"10.18632/aging.101034","title":"Deep biomarkers of aging are population-dependent","year":2016,"lang":"en","type":"letter","venue":"Aging","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Population ageing; Population; Medicine; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002799975,0.0005386139,0.0007212418,0.000412288,0.001294459,0.001944446,0.001009947,0.0101341,0.005241837],"category_scores_gemma":[0.01838574,0.0003470385,0.0004546255,0.0003191713,0.001644809,0.002239801,0.001021345,0.01949214,0.004965201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001837696,"about_ca_system_score_gemma":0.001613936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198675,"about_ca_topic_score_gemma":0.002003836,"domain_scores_codex":[0.9987304,0.0004158551,0.0001089513,0.0002434953,0.0004102701,0.00009105709],"domain_scores_gemma":[0.9926229,0.004553664,0.0004544889,0.0003182527,0.001497126,0.000553576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00006574982,0.0000139429,0.0004100172,0.00003746397,0.0000101811,0.0003300458,0.00003972896,0.00006104827,0.0001854952,0.003520442,0.9604756,0.03485036],"study_design_scores_gemma":[0.00009746947,0.00005751423,0.00121402,0.0002793451,0.00002882481,0.00130647,0.0000990468,0.0007422934,0.0008014272,0.02260769,0.9727217,0.00004409925],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0003932332,0.002980429,0.0005584831,0.9679258,0.026152,0.000006846841,0.0001176514,0.00006885189,0.001796632],"genre_scores_gemma":[0.01142459,0.005627163,0.000866364,0.8739039,0.09539637,0.00004786981,0.0001313282,0.00006690556,0.0125355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0101341,"threshold_uncertainty_score":0.01753569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00973338182177907,"score_gpt":0.2299897263449232,"score_spread":0.2202563445231441,"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."}}