{"id":"W4407631902","doi":"10.1101/2025.02.17.633647","title":"Adaptive time course of the skeletal muscle proteome during programmed resistance training in rats","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Adipose Tissue and Metabolism","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Proteome; Resistance training; Skeletal muscle; Training (meteorology); Course (navigation); Resistance (ecology); Computer science; Biology; Cell biology; Bioinformatics; Anatomy; Engineering; Endocrinology; Ecology; Physics","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":[0.0001370154,0.0002529745,0.0002648725,0.000333528,0.00009551414,0.0001896422,0.0001235296,0.0001849251,0.001134139],"category_scores_gemma":[0.00011418,0.0001696602,0.0002673464,0.0001785716,0.000255393,0.0001859208,0.0001951392,0.0004237629,0.0002802398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001609604,"about_ca_system_score_gemma":0.0001365661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006842858,"about_ca_topic_score_gemma":0.001035701,"domain_scores_codex":[0.9999272,0.000006272837,0.00000289898,0.00002845238,0.00001449655,0.00002071593],"domain_scores_gemma":[0.999892,0.000009446498,0.00003707883,0.00001272491,0.00001621319,0.0000325025],"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":[0.000582463,0.00002904,0.0008488959,0.00002294088,0.000009931226,0.00003172884,0.00002486165,0.0000564165,0.9964172,0.00002938036,0.00003073039,0.001916265],"study_design_scores_gemma":[0.00005275479,0.002175011,0.2046768,0.00002296726,0.00007604438,0.0003215581,0.0002434179,0.001953024,0.788361,0.0002425466,0.001841368,0.00003365055],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972311,0.0002992821,0.001612043,0.00003641093,0.00001519345,0.000008662435,0.0004345399,0.00004822822,0.0003145007],"genre_scores_gemma":[0.9920666,0.0003958635,0.002104614,0.00006830133,0.00001091899,0.00007466887,0.0008499026,0.00005009271,0.004379038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001134139,"threshold_uncertainty_score":0.003794014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552645346772139,"score_gpt":0.2408670324499241,"score_spread":0.2253405789822027,"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."}}