{"id":"W4386360264","doi":"10.1101/2023.08.30.555622","title":"Human Ageing Genomic Resources: updates on key databases in ageing research","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Longevity; Ageing; Model organism; Biology; Database; Gene; Computational biology; Genomics; Healthy ageing; Senescence; Genetics; Genome; Computer science","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":[0.005809171,0.00194071,0.00204302,0.01056813,0.0007163515,0.003155726,0.002664873,0.002055833,0.08090759],"category_scores_gemma":[0.01868453,0.001193836,0.001053754,0.01383271,0.000411501,0.002772375,0.004451119,0.00158791,0.08287399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000913237,"about_ca_system_score_gemma":0.003749454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004460238,"about_ca_topic_score_gemma":0.004965669,"domain_scores_codex":[0.99757,0.0005663633,0.0007155237,0.0004130575,0.0005837301,0.0001513218],"domain_scores_gemma":[0.9918523,0.003139555,0.0007654618,0.00168626,0.001683901,0.0008724608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006883219,0.00007276617,0.002053647,0.007658152,0.0002159847,0.0002624602,0.0004426776,0.0004453279,0.003359654,0.004892468,0.8559589,0.1239496],"study_design_scores_gemma":[0.0001760371,0.000027033,0.004074776,0.0009759737,0.0001506693,0.0002868422,0.00009239862,0.00030786,0.00150631,0.004520552,0.9878179,0.00006364591],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001801623,0.007871915,0.01184083,0.001439348,0.0002904883,0.0002376143,0.9414655,0.02342455,0.01162818],"genre_scores_gemma":[0.003263327,0.004604194,0.02324442,0.0007227816,0.0001857381,0.0004309444,0.959281,0.003903687,0.004363938],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08090759,"threshold_uncertainty_score":0.2706628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05445943728570744,"score_gpt":0.2956221664426257,"score_spread":0.2411627291569182,"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."}}