{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003449253,0.003761543,0.001991658,0.002822773,0.00188953,0.001316653,0.002620015,0.001075753,0.02151111],"category_scores_gemma":[0.002535719,0.00146409,0.001372068,0.002292682,0.001259539,0.001202653,0.00149858,0.003805394,0.02261457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005537532,"about_ca_system_score_gemma":0.001622467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009439776,"about_ca_topic_score_gemma":0.002850248,"domain_scores_codex":[0.9967327,0.0007027122,0.0005600879,0.000859977,0.0007145974,0.0004298698],"domain_scores_gemma":[0.9985369,0.0004003404,0.0001209182,0.0003610776,0.0004736231,0.0001070651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007915482,0.0003378664,0.0007320344,0.001419317,0.0001081383,0.0007296841,0.0004451053,0.0004422318,0.9530079,0.002051846,0.01431512,0.02561926],"study_design_scores_gemma":[0.0002797517,0.0009029376,0.00982694,0.0005847568,0.0003202916,0.001969337,0.0002052762,0.004060597,0.5524519,0.001810873,0.4273595,0.0002279041],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08619075,0.01226271,0.8179902,0.001295009,0.002128771,0.0179495,0.02114277,0.01233587,0.0287044],"genre_scores_gemma":[0.03667275,0.01026284,0.8200102,0.00111509,0.0004240415,0.03015042,0.05304972,0.003917035,0.04439792],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02151111,"threshold_uncertainty_score":0.07196182,"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."}}