{"id":"W4250781766","doi":"10.36866/pn.80.22","title":"HIT to get fit: metabolic adaptations to low-volume high-intensity interval training","year":2010,"lang":"en","type":"article","venue":"Physiology News","topic":"Cardiovascular and exercise physiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health","keywords":"High-intensity interval training; Intensity (physics); Volume (thermodynamics); Training (meteorology); Adaptation (eye); Interval (graph theory); Computer science; Mathematics; Psychology; Medicine; Geography; Physiology; Physics; Meteorology; Neuroscience; Optics; Thermodynamics","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":[0.0003719993,0.0004519922,0.0003658675,0.0001901921,0.0003330596,0.0006901937,0.0003456482,0.0008312442,0.004846277],"category_scores_gemma":[0.001395163,0.0002494788,0.0003081535,0.0002121919,0.0004867825,0.0003566121,0.0006105789,0.00143592,0.0006887659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002430639,"about_ca_system_score_gemma":0.0002102292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002002586,"about_ca_topic_score_gemma":0.001801313,"domain_scores_codex":[0.9998728,0.00004565325,0.000005154002,0.00002915228,0.00001554735,0.00003176478],"domain_scores_gemma":[0.9997415,0.000071018,0.00004463885,0.00003346045,0.00002184168,0.00008753952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.05981994,0.02399162,0.1289262,0.003163126,0.002935415,0.001959498,0.004115473,0.00257932,0.2263051,0.005308673,0.05140056,0.4894952],"study_design_scores_gemma":[0.0006692409,0.006886512,0.9728568,0.0002274423,0.0003115745,0.0004063586,0.001147468,0.001743378,0.003811644,0.003500075,0.008372823,0.00006668331],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815667,0.006172865,0.001144325,0.004594722,0.001025502,0.00006276899,0.0005914361,0.0001584261,0.004683274],"genre_scores_gemma":[0.9806823,0.004082549,0.001881602,0.002030807,0.0004956656,0.0002262509,0.0006672986,0.0001342327,0.009799295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004846277,"threshold_uncertainty_score":0.0162124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02953725969784142,"score_gpt":0.28278109451446,"score_spread":0.2532438348166186,"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."}}