{"id":"W3083682358","doi":"10.1113/ep089010","title":"Skeletal muscle ferritin abundance is tightly related to plasma ferritin concentration in adults with obesity","year":2020,"lang":"en","type":"article","venue":"Experimental Physiology","topic":"Iron Metabolism and Disorders","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Canada Economic Development for Quebec Regions; Canadian Institutes of Health Research; National Institutes of Health; American Diabetes Association","keywords":"Ferritin; Skeletal muscle; Obesity; Endocrinology; Abundance (ecology); Internal medicine; Medicine; Biology; Chemistry; Physiology; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007389403,0.0002316816,0.0003670501,0.0003305915,0.0001280536,0.0003839101,0.0002469294,0.000368091,0.0009449548],"category_scores_gemma":[0.00216721,0.0001911996,0.0002624199,0.000416585,0.0003080721,0.0004015643,0.0002540619,0.0004143399,0.0001693424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009403724,"about_ca_system_score_gemma":0.0001063211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001206684,"about_ca_topic_score_gemma":0.001432061,"domain_scores_codex":[0.9997548,0.0000687792,0.00002627363,0.00008810267,0.00004027311,0.00002179187],"domain_scores_gemma":[0.9985929,0.0002807725,0.0007357332,0.00009938385,0.0001681118,0.0001231248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002265664,0.00001497546,0.9944754,0.00003260191,0.0001042242,0.00006755705,0.00008855882,0.00003576592,0.001445537,0.00002454806,0.00009355068,0.003390698],"study_design_scores_gemma":[0.000002076926,0.00006959865,0.9994136,0.00000712194,0.00002781543,0.0001835095,0.00005309596,0.00007445654,0.00006568011,0.00002629586,0.00007517983,0.000001594387],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959047,0.002878507,0.0004171079,0.0001867027,0.0000174636,0.000004851409,0.0001447447,0.000007929051,0.000437997],"genre_scores_gemma":[0.9990309,0.0003958705,0.0002250812,0.00004755197,0.00003250984,0.00000335448,0.000083669,0.000002462926,0.0001786584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001206684,"threshold_uncertainty_score":0.003907919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008938145128447319,"score_gpt":0.2571134024611521,"score_spread":0.2481752573327047,"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."}}