{"id":"W2157202568","doi":"10.1136/bjsm.2009.064774","title":"Optimal pacing strategy: from theoretical modelling to reality in 1500-m speed skating","year":2009,"lang":"en","type":"article","venue":"British Journal of Sports Medicine","topic":"Sports Performance and Training","field":"Medicine","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Speed skating; Anaerobic exercise; Athletes; Simulation; Drag; Power (physics); Range (aeronautics); Time trial; Aerodynamics; Computer science; Drag coefficient; Mathematics; Statistics; Physical therapy; Mechanics; Medicine; Engineering; Physics; Heart rate","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":[0.0005652927,0.0003055184,0.0002735967,0.0002606473,0.0001441184,0.0006753818,0.0005391406,0.0004759924,0.001158921],"category_scores_gemma":[0.001953748,0.0002497768,0.0002344743,0.0001236993,0.0004864207,0.000524858,0.0003691102,0.0002696565,0.0001943063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004775952,"about_ca_system_score_gemma":0.0005650443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003398629,"about_ca_topic_score_gemma":0.001886551,"domain_scores_codex":[0.9998423,0.0000734216,0.000009522956,0.00003492853,0.00002372743,0.00001607872],"domain_scores_gemma":[0.9997149,0.0001977087,0.00003916392,0.00001305281,0.00002153803,0.00001354464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003476661,0.0001806415,0.01865222,0.0002428809,0.000034778,0.0001746825,0.0004873092,0.9472287,0.007441224,0.004982037,0.0001900846,0.02003784],"study_design_scores_gemma":[0.00001861,0.0002904084,0.009122809,0.00005224886,0.00001648636,0.00008244016,0.000139385,0.9860325,0.0009446251,0.002888753,0.0003985763,0.00001314894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9100336,0.000255105,0.085184,0.0001101271,0.000008318344,0.00005167167,0.00005685358,0.00005575287,0.0042445],"genre_scores_gemma":[0.993028,0.0001367893,0.006416504,0.00001089957,0.000002357338,0.00004785949,0.00004039092,0.00001067417,0.0003065236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003398629,"threshold_uncertainty_score":0.006757677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02953002035428438,"score_gpt":0.3003974442179499,"score_spread":0.2708674238636655,"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."}}