{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000150873,0.0001775325,0.0004345608,0.0001902626,0.0003253928,0.00001735559,0.00004747177,0.00009879304,0.000002430106],"category_scores_gemma":[0.00007812419,0.0001258451,0.00005955203,0.0001851172,0.0001037122,0.00006077572,0.00002699862,0.0002381133,0.00002553736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001719219,"about_ca_system_score_gemma":0.000158243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001229512,"about_ca_topic_score_gemma":0.00007262748,"domain_scores_codex":[0.9990609,0.0001040795,0.0001870894,0.0003003671,0.00007426029,0.0002732621],"domain_scores_gemma":[0.9992625,0.0003285086,0.00008531563,0.00007831509,0.0001123923,0.0001330289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0006126846,0.00002757284,0.01381708,0.00008074576,0.0001220463,0.000004127683,0.004652144,0.00002266428,0.003414916,0.975071,0.002125194,0.00004978748],"study_design_scores_gemma":[0.008572523,0.01002275,0.3977979,0.001344607,0.00025029,0.000039504,0.04279532,0.2419679,0.003736268,0.29144,0.0009476414,0.001085389],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827474,0.0004283265,0.01151036,0.001157474,0.0003360799,0.0008442665,0.00003381129,0.00009514279,0.002847128],"genre_scores_gemma":[0.9982833,0.0004116973,0.0002963833,0.0004991125,0.0001117085,0.00006155487,0.0001149917,0.000007344826,0.0002138911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6836311,"threshold_uncertainty_score":0.5131814,"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."}}