{"id":"W4379599550","doi":"10.1093/icb/icad058","title":"Natural Animal Populations as Model Systems for Understanding Early Life Adversity Effects on Aging","year":2023,"lang":"en","type":"article","venue":"Integrative and Comparative Biology","topic":"Birth, Development, and Health","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"H2020 European Research Council; National Institute on Aging; National Institutes of Health; National Science Foundation","keywords":"Confounding; Natural (archaeology); Disease; Psychology; Human health; Healthy aging; Gerontology; Biology; Medicine; Environmental health","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":[0.001842996,0.0004759096,0.0005411663,0.001000128,0.0009378917,0.001028763,0.0009481619,0.000736677,0.002525632],"category_scores_gemma":[0.001743185,0.0002747696,0.0004754422,0.0006721888,0.001239146,0.001067227,0.001301927,0.00103133,0.0003466911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007179538,"about_ca_system_score_gemma":0.0007381429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003424913,"about_ca_topic_score_gemma":0.01303659,"domain_scores_codex":[0.9989324,0.0007182931,0.00004183602,0.0001817669,0.00007440785,0.00005123129],"domain_scores_gemma":[0.9989773,0.0004186785,0.0002242577,0.0002014761,0.00007366613,0.0001046601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001492779,0.002951051,0.303278,0.003089308,0.002347387,0.00171169,0.009014166,0.01604969,0.2698779,0.2070183,0.01216427,0.1710054],"study_design_scores_gemma":[0.0003632738,0.01157066,0.4416925,0.001893471,0.002061646,0.003277647,0.01421558,0.03130747,0.03624385,0.2346949,0.2222933,0.0003857918],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7867391,0.01439533,0.1636931,0.00274555,0.0006174575,0.001349392,0.004645174,0.0002232136,0.02559166],"genre_scores_gemma":[0.8537241,0.01201511,0.1222617,0.001550067,0.000144622,0.00325689,0.002105499,0.00007380962,0.004868215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003424913,"threshold_uncertainty_score":0.00974679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2356022824852919,"score_gpt":0.4106548981149451,"score_spread":0.1750526156296532,"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."}}