{"id":"W4322731998","doi":"10.51224/srxiv.266","title":"Fatigue in elite fencing","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Sports Performance and Training","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Fencing; Elite; Psychology; Business; Political science; Computer science","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":[0.0003586181,0.0003423462,0.0003976277,0.0005847661,0.0004935156,0.0003697219,0.0002640918,0.0004622602,0.002275911],"category_scores_gemma":[0.001053857,0.0001052307,0.0002211271,0.0003523807,0.0003202949,0.0002459494,0.0003970166,0.0001436241,0.0001696828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004318753,"about_ca_system_score_gemma":0.0003207355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009137433,"about_ca_topic_score_gemma":0.0101729,"domain_scores_codex":[0.9997643,0.00003423033,0.00001379586,0.00006186188,0.00005232101,0.00007353921],"domain_scores_gemma":[0.9997762,0.00004028496,0.0000726071,0.000008214874,0.0000536402,0.00004908993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00142028,0.0002836027,0.8814911,0.0006520782,0.000133977,0.001565843,0.002894456,0.000730821,0.02111552,0.0001059316,0.0004597807,0.08914661],"study_design_scores_gemma":[0.00000661004,0.000978853,0.9966725,0.00003704957,0.00001295825,0.000712888,0.0006566101,0.0001172045,0.0003157523,0.00004065178,0.0004430396,0.000005964661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983305,0.0008907517,0.0001311924,0.00001820626,0.000006428372,0.000009981375,0.000047671,0.000001551797,0.0005635851],"genre_scores_gemma":[0.9989066,0.0003091528,0.0001136164,0.0000313095,0.00001352569,0.00001596657,0.00008716265,8.042322e-7,0.0005218467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009137433,"threshold_uncertainty_score":0.01816845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1592833848650853,"score_gpt":0.3730403594943565,"score_spread":0.2137569746292713,"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."}}