{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004368879,0.0001184936,0.0003144147,0.0003353449,0.0001025543,0.0001239527,0.0004094687,0.0001951633,0.00008887263],"category_scores_gemma":[0.002087824,0.00009879749,0.0001721446,0.0005143716,0.0001339065,0.0002055171,0.00005216933,0.00170141,0.00000992731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003283773,"about_ca_system_score_gemma":0.0003794166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000178366,"about_ca_topic_score_gemma":0.000003967109,"domain_scores_codex":[0.998037,0.0001058508,0.0007119045,0.0002356559,0.0005088054,0.0004007629],"domain_scores_gemma":[0.9970558,0.001770611,0.0001872145,0.0002490056,0.0006265684,0.0001107441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007429424,0.0001634144,0.0002693768,0.0009536224,0.00006269829,0.00003111059,0.0004344975,0.00004968053,0.6769161,0.3171046,0.0003029746,0.002969012],"study_design_scores_gemma":[0.0006872311,0.0002933957,0.00004375654,0.001205869,0.000008475872,0.00002693147,0.0003925655,0.00295348,0.1760018,0.8092902,0.008934451,0.0001618788],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08994695,0.0009922021,0.9030463,0.003890587,0.00004073643,0.001436473,0.00007297036,0.00004040819,0.0005333816],"genre_scores_gemma":[0.4034118,0.0001517115,0.5946561,0.00001869612,0.0002581578,0.001203844,0.000001782235,0.00003489883,0.0002629946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5009143,"threshold_uncertainty_score":0.7391875,"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."}}