{"id":"W2939897467","doi":"10.1016/j.mex.2019.04.010","title":"RELi protocol: Optimization for protein extraction from white, brown and beige adipose tissues","year":2019,"lang":"en","type":"article","venue":"MethodsX","topic":"Adipose Tissue and Metabolism","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada; Diabetes Canada; Université de Montréal; Canadian Diabetes Association; Natural Sciences and Engineering Research Council of Canada; Foundation Fighting Blindness","keywords":"Adipose tissue; Western blot; White adipose tissue; Biology; Protein purification; Extraction (chemistry); Bioinformatics; Computational biology; Cell biology; Chemistry; Biochemistry; Chromatography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004673484,0.0001609623,0.0003502123,0.00008732337,0.00006082963,0.00002707563,0.00004923522,0.0001622653,0.0006769539],"category_scores_gemma":[0.0002601349,0.0001373257,0.00005803216,0.0001145087,0.00003548958,0.0002080585,0.0000248767,0.0001426203,0.0000355606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000221548,"about_ca_system_score_gemma":0.00004565523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000728147,"about_ca_topic_score_gemma":0.000003488022,"domain_scores_codex":[0.9988903,0.0001270253,0.0002624961,0.0003585918,0.0001612889,0.0002003248],"domain_scores_gemma":[0.9992405,0.00008742764,0.0001307982,0.0003032352,0.0001203217,0.0001177049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002506276,0.0003882446,0.004917778,0.0009149856,0.0002467108,0.0000219206,0.002049851,0.0002454668,0.3184597,0.001077142,0.00227455,0.6668974],"study_design_scores_gemma":[0.006523347,0.001006829,0.01073845,0.0003847815,0.0004342959,0.00003859655,0.0001186501,0.008287524,0.1474721,0.002735399,0.8218313,0.0004286969],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1076803,0.001688734,0.6464176,0.001528167,0.0005495388,0.2350512,0.00005080175,0.00036426,0.006669394],"genre_scores_gemma":[0.003479256,0.00001470001,0.9484248,0.0001848079,0.0004578025,0.03008826,0.00007619421,0.00005536685,0.01721882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8195568,"threshold_uncertainty_score":0.7412171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0327543174312314,"score_gpt":0.3686802148493699,"score_spread":0.3359258974181385,"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."}}