{"id":"W2995379599","doi":"10.1123/ijspp.2019-0260","title":"Optimizing Interval Training Through Power-Output Variation Within the Work Intervals","year":2020,"lang":"en","type":"article","venue":"International Journal of Sports Physiology and Performance","topic":"Sports Performance and Training","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"High-intensity interval training; Interval training; Perceived exertion; Work (physics); VO2 max; Respiratory minute volume; Cadence; Physical therapy; Work output; Heart rate; Medicine; Rating of perceived exertion; Hyperpnea; Respiratory exchange ratio; Animal science; Physical medicine and rehabilitation; Internal medicine; Respiratory system; Blood pressure","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003736749,0.0001429584,0.0003235768,0.00006714269,0.00008526185,0.00002580853,0.0002077473,0.00007358594,0.0001342849],"category_scores_gemma":[0.00003627467,0.00009293488,0.000116061,0.0001133523,0.0001290353,0.0004178027,0.00005778514,0.0004877476,0.000004999419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002319399,"about_ca_system_score_gemma":0.00009798645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000161323,"about_ca_topic_score_gemma":1.271086e-7,"domain_scores_codex":[0.9987772,0.00001099219,0.0005857736,0.000151745,0.0003241158,0.0001502372],"domain_scores_gemma":[0.999009,0.00003205771,0.0005516628,0.00008566174,0.0002414036,0.00008018622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01572168,0.0002695907,0.5856069,0.0002053948,0.002133029,0.0007717826,0.25875,0.007483675,0.008215675,0.00157649,0.001007634,0.1182581],"study_design_scores_gemma":[0.001442433,0.0007463268,0.9885757,0.0006256035,0.00009301576,0.00102387,0.0017837,0.002285103,0.001248452,0.0001936322,0.001833495,0.0001486566],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994614,0.0002238863,0.000381587,0.00303175,0.001015387,0.00007655119,0.000001203297,0.00001617336,0.0006394754],"genre_scores_gemma":[0.9930333,0.0003122734,0.001390924,0.004300041,0.0009074169,0.000002209358,0.000007268627,0.00001314201,0.00003337402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4029688,"threshold_uncertainty_score":0.3789774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04025538693147715,"score_gpt":0.2860171899634126,"score_spread":0.2457618030319355,"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."}}