{"id":"W1687567765","doi":"10.1139/h09-023","title":"Molecular responses to strength and endurance training: Are they incompatible?This paper article is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.","year":2009,"lang":"en","type":"review","venue":"Applied Physiology Nutrition and Metabolism","topic":"Muscle metabolism and nutrition","field":"Biochemistry, Genetics and Molecular Biology","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Sports Commission; GlaxoSmithKline","keywords":"Endurance training; Training (meteorology); Adaptation (eye); Selection (genetic algorithm); Strength training; Mode (computer interface); Resistance training; Psychology; Neuroscience; Computer science; Physical medicine and rehabilitation; Medicine; Physical therapy; Human–computer interaction; Artificial intelligence; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005338406,0.0003222552,0.001251372,0.000188747,0.00006508597,0.00003303906,0.0001882317,0.0002724813,0.0003898981],"category_scores_gemma":[0.0002185602,0.0002569908,0.000111806,0.0002426133,0.0002683871,0.00002462348,0.0000896331,0.000267643,2.997626e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005463007,"about_ca_system_score_gemma":0.0001229798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002767829,"about_ca_topic_score_gemma":0.000007722094,"domain_scores_codex":[0.9981437,0.0002526669,0.0006798938,0.0004439544,0.000292676,0.0001870662],"domain_scores_gemma":[0.9986756,0.00004870609,0.0006105676,0.0002143202,0.000348291,0.0001024656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005233733,0.0004844552,0.00001095383,0.008324377,0.0003143772,0.000001652362,0.0005633611,5.536137e-7,0.7187381,0.0005756657,0.001075464,0.2693877],"study_design_scores_gemma":[0.005272873,0.0001335001,0.005988111,0.01155951,0.002149347,0.0001173115,0.0005011072,0.00001111565,0.2045138,0.003127037,0.7657968,0.0008294414],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.2070212,0.7913196,0.000007067496,0.0005578274,0.00005759242,0.0006824884,0.0001389055,0.000004048086,0.0002113354],"genre_scores_gemma":[0.1770395,0.82202,0.0002339731,0.0002414359,0.0001957768,0.00007656971,0.0001380087,0.00001809845,0.00003663841],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7647213,"threshold_uncertainty_score":0.9999883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149801703905927,"score_gpt":0.288144400682471,"score_spread":0.2666463836434117,"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."}}