{"id":"W3207612198","doi":"10.1093/molbev/msab302","title":"Aging at Evolutionary Crossroads: Longitudinal Gene Co-expression Network Analyses of Proximal and Ultimate Causes of Aging in Bats","year":2021,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Bat Biology and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Biology; Natural selection; Evolutionary biology; Phenotype; Gene; Gene expression; Population; Selection (genetic algorithm); Transcriptome; Ageing; Ecology; Genetics; Demography","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":[0.0004642112,0.0001452052,0.0003348594,0.0009839418,0.0004055684,0.0003924095,0.0002198945,0.0002817446,0.0007733277],"category_scores_gemma":[0.0008344428,0.0001364916,0.0004221876,0.001151226,0.0003949239,0.0004594423,0.0005220173,0.0003621572,0.0001695418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003268387,"about_ca_system_score_gemma":0.0002598579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002633144,"about_ca_topic_score_gemma":0.005034683,"domain_scores_codex":[0.9997761,0.00004249894,0.00001171912,0.0001085707,0.00002485676,0.00003625198],"domain_scores_gemma":[0.9995435,0.0001278335,0.0001470425,0.00004411746,0.00007470714,0.00006280505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005002384,0.0001181495,0.6033844,0.0003827004,0.000626571,0.0003807343,0.001660271,0.01152452,0.3354856,0.0027989,0.001205373,0.04193247],"study_design_scores_gemma":[0.000006282512,0.0000850879,0.9652213,0.0000203352,0.000126553,0.000215277,0.0006042049,0.02508088,0.004517023,0.002336009,0.001761896,0.00002525645],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912289,0.0006761591,0.006409352,0.00008874807,0.000006946528,0.00001034001,0.0009790096,0.00005161028,0.0005488311],"genre_scores_gemma":[0.9928179,0.0002147306,0.004978618,0.00007179671,0.00001142601,0.00003721994,0.001505337,0.00001821711,0.0003447859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002633144,"threshold_uncertainty_score":0.005235672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637549456853859,"score_gpt":0.3007132438627489,"score_spread":0.2743377492942103,"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."}}