{"id":"W2900536852","doi":"10.18632/aging.101646","title":"Aging and drug discovery","year":2018,"lang":"en","type":"article","venue":"Aging","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Instituto de Salud Carlos III; Novo Nordisk; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Novo Nordisk Fonden; Fondation pour la Recherche Médicale; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Leids Universitair Medisch Centrum; European Commission; Deutsche Forschungsgemeinschaft; Strong; National Institute on Aging; Glenn Foundation for Medical Research; National Science Foundation","keywords":"Transformative learning; Drug discovery; Psychological intervention; Longevity; Process (computing); Successful aging; Engineering ethics; Political science; Medicine; Gerontology; Psychology; Computer science; Biology; Engineering; Bioinformatics","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":[0.0001433779,0.0001156601,0.00008532447,0.00002878211,0.0001418359,0.00007921911,0.00011967,0.00004291107,0.00001093662],"category_scores_gemma":[0.00002219641,0.0001153414,0.00003179984,0.00003780264,0.0001365312,0.000007539853,0.0001727228,0.00005808536,0.00001559375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000553906,"about_ca_system_score_gemma":0.00002113994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005181507,"about_ca_topic_score_gemma":0.00007223261,"domain_scores_codex":[0.9992659,0.00002620808,0.0001044536,0.0002990534,0.00007821444,0.0002261888],"domain_scores_gemma":[0.9996035,0.000006565228,0.00003601867,0.0002585886,0.00003652344,0.00005887836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001395018,0.00003771378,0.0591882,0.00004490854,0.00007033997,0.000007674118,0.002599124,0.0000291401,0.9252183,0.0004454844,0.004732077,0.007613129],"study_design_scores_gemma":[0.000622413,0.0001410245,0.02057425,0.00004607782,0.00004700466,0.00005190213,0.0004840195,0.0003720591,0.9007009,0.0008863003,0.07550827,0.0005657937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874114,0.0008942148,0.009729966,0.0003989497,0.0002469757,0.00005970035,0.000002854965,0.00001855431,0.001237344],"genre_scores_gemma":[0.9950119,0.0001566913,0.001305491,0.000825282,0.0007286219,0.000002617189,0.00001431183,0.00002250879,0.001932549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07077619,"threshold_uncertainty_score":0.4703487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006699696094744107,"score_gpt":0.2302485548086328,"score_spread":0.2235488587138887,"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."}}