{"id":"W2757145102","doi":"10.3389/fnut.2017.00049","title":"Carbohydrate-Restriction with High-Intensity Interval Training: An Optimal Combination for Treating Metabolic Diseases?","year":2017,"lang":"en","type":"article","venue":"Frontiers in Nutrition","topic":"Diet and metabolism studies","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Postprandial; Cardiorespiratory fitness; Medicine; High-intensity interval training; Interval training; Carbohydrate; Internal medicine; Physical therapy; Endocrinology; Insulin","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":[0.0001368197,0.0001578881,0.0005131934,0.0002123269,0.0003108153,0.00007135232,0.00008475082,0.0000745328,0.000001906648],"category_scores_gemma":[0.0001589269,0.0001386698,0.0000814534,0.00008912414,0.0001134184,0.0004755676,0.00001910243,0.0001114029,4.69081e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006898423,"about_ca_system_score_gemma":0.00003434328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001069846,"about_ca_topic_score_gemma":0.00002474649,"domain_scores_codex":[0.9990821,0.00002970599,0.0002028667,0.0002826122,0.0001647367,0.0002379406],"domain_scores_gemma":[0.9992632,0.00001315836,0.0001685369,0.0002565298,0.0001973219,0.0001012455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03008476,0.008889806,0.721278,0.002275607,0.001337469,0.000169288,0.008863134,0.0001357681,0.009364907,0.006774612,0.0203227,0.1905039],"study_design_scores_gemma":[0.01755462,0.001813133,0.9556599,0.0007655921,0.001055167,0.000007555466,0.005613463,0.008665409,0.002901939,0.002998084,0.002577819,0.0003872722],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875562,0.001037105,0.008154252,0.000542897,0.001472226,0.0008836681,0.00004323543,0.00006950778,0.0002409613],"genre_scores_gemma":[0.9793666,0.0006800978,0.01880417,0.00004300821,0.0004664166,0.0002691238,0.0002776802,0.00002316123,0.00006972066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2343819,"threshold_uncertainty_score":0.5654789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02370771814313586,"score_gpt":0.2778872596603666,"score_spread":0.2541795415172308,"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."}}