{"id":"W4214772954","doi":"10.1093/geroni/igz038.325","title":"THE PROBLEM OF INTEGRATING OF BIOLOGICAL AND CLINICAL MARKERS OF AGEING","year":2019,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Ageing; Biological age; Gompertz function; Frailty Index; Computer science; Computational biology; Biology; Gerontology; Medicine; Machine learning; Genetics","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":[0.003143144,0.00005754066,0.0001443481,0.00005731613,0.00003053356,0.000003947498,0.00009570757,0.00004453598,0.00008802179],"category_scores_gemma":[0.0002998296,0.00004159467,0.00001685463,0.0005325479,0.0002905765,0.00009121387,0.0001280037,0.0001658081,0.000004416105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003905622,"about_ca_system_score_gemma":0.000009549809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001389211,"about_ca_topic_score_gemma":0.00002525202,"domain_scores_codex":[0.9986036,0.0001464206,0.0008009215,0.0001820078,0.0001438885,0.0001230923],"domain_scores_gemma":[0.9988651,0.0005513976,0.0004382966,0.0001186075,0.00001488756,0.00001166421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001097217,0.00001774829,0.886211,0.00001630855,0.000003576079,3.109766e-7,0.0002629707,0.00005070725,0.0216482,0.0005444943,0.000003166263,0.09123059],"study_design_scores_gemma":[0.0002576101,0.00007815631,0.9928028,0.0001415868,0.000001730577,6.504816e-7,0.001111245,0.001348446,0.002973206,0.0009843219,0.0002489515,0.00005128913],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938092,0.00001478659,0.0008257095,0.0001688222,0.00002683371,0.0002305719,9.074113e-7,0.000002960199,0.004920251],"genre_scores_gemma":[0.9960361,0.00004402293,0.003825678,0.00006381493,0.000005098823,0.000003316631,0.000001332023,0.000004072168,0.00001662839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1065918,"threshold_uncertainty_score":0.1696181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03280803453609306,"score_gpt":0.3211522192360538,"score_spread":0.2883441846999608,"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."}}