{"id":"W4394228747","doi":"10.6084/m9.figshare.20337194","title":"APPLICATION OF FUNCTIONAL TRAINING IN SOCCER FITNESS","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Sports Performance and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Training (meteorology); Functional training; Physical medicine and rehabilitation; Psychology; Computer science; Geography; Medicine; Meteorology","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":[0.002495522,0.001663856,0.002495983,0.003796038,0.0006583864,0.002164237,0.002861969,0.001928779,0.1039185],"category_scores_gemma":[0.02006351,0.000465734,0.003588046,0.005220965,0.0003710056,0.001240351,0.002061215,0.001824503,0.02057222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001109034,"about_ca_system_score_gemma":0.002532955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01423593,"about_ca_topic_score_gemma":0.01907229,"domain_scores_codex":[0.9974205,0.0005862157,0.0006666953,0.0007077516,0.0003608487,0.0002579649],"domain_scores_gemma":[0.9936408,0.002889771,0.001159882,0.0007241157,0.001321838,0.0002636198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002767962,0.0002419058,0.03190827,0.0510719,0.002295725,0.00008993974,0.00008603203,0.001662769,0.0002240395,0.00103628,0.867422,0.04119323],"study_design_scores_gemma":[0.0110551,0.0006028423,0.1841156,0.0250167,0.006280787,0.0004827008,0.0003278586,0.004359957,0.0009145266,0.005241944,0.761312,0.0002898893],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009055823,0.001172348,0.0001599358,0.0001859637,0.00007573697,0.0001636624,0.9961593,0.0001683487,0.001009127],"genre_scores_gemma":[0.01326081,0.001255853,0.001237868,0.0003264447,0.00009198569,0.002692761,0.9790325,0.00009467133,0.002007157],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1039185,"threshold_uncertainty_score":0.347642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08155221014054158,"score_gpt":0.3101194484298491,"score_spread":0.2285672382893075,"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."}}