{"id":"W4402363242","doi":"10.1021/acs.jproteome.4c00363","title":"A Framework for Quality Control in Quantitative Proteomics","year":2024,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Army Research Office; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; University of Washington; National Institute on Aging; Intelligence Advanced Research Projects Activity; National Institutes of Health","keywords":"Proteomics; Quality (philosophy); Quantitative proteomics; Computer science; Computational biology; Control (management); Data science; Chemistry; Biology; Artificial intelligence; Biochemistry; Physics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1825964,0.003851009,0.003531945,0.008004145,0.003442775,0.01720356,0.0104542,0.006025363,0.003807461],"category_scores_gemma":[0.1153629,0.002601783,0.004742287,0.006042067,0.02098236,0.01205196,0.01172447,0.01232053,0.003287759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008758577,"about_ca_system_score_gemma":0.01778249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005704168,"about_ca_topic_score_gemma":0.002875316,"domain_scores_codex":[0.8613954,0.07409669,0.01398028,0.01321545,0.03500617,0.002306035],"domain_scores_gemma":[0.8478079,0.07084582,0.01185678,0.03305138,0.03342395,0.003014239],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001311077,0.0001355076,0.0009549655,0.001571616,0.0001986023,0.0004158489,0.00153634,0.01315088,0.004392236,0.8680136,0.01129363,0.09820563],"study_design_scores_gemma":[0.00009685824,0.000211248,0.000674982,0.00123705,0.0001042299,0.0003768446,0.0002934709,0.03946782,0.005090688,0.7928414,0.1594159,0.0001896009],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003567117,0.001509465,0.9912573,0.001948655,0.0002875915,0.0003518848,0.0001673379,0.001251741,0.002869391],"genre_scores_gemma":[0.01450027,0.001616462,0.9780878,0.001222111,0.000673403,0.001719826,0.0004978098,0.0004903084,0.001192113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8174036,"threshold_uncertainty_score":0.9656736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1844919372238568,"score_gpt":0.5369115189509517,"score_spread":0.3524195817270949,"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."}}