{"id":"W4225545880","doi":"10.1038/s41597-022-01216-6","title":"A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Spinal Cord Injury BC","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek; Agence Nationale de la Recherche","keywords":"Computer science; Data acquisition; Benchmark (surveying); Data mining; Proteomics; Data science; Biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003964695,0.0001277992,0.0001193887,0.0000878115,0.0005055347,0.0002207685,0.001537796,0.00003603692,0.0009641877],"category_scores_gemma":[0.0000152726,0.0001407861,0.00001799938,0.0002963186,0.0001375212,0.0003159735,0.001668676,0.0003290077,0.00002668024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097759,"about_ca_system_score_gemma":0.0001077335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003574375,"about_ca_topic_score_gemma":0.00002225907,"domain_scores_codex":[0.9983373,0.00003183135,0.0002308293,0.0008481873,0.0003096574,0.0002422483],"domain_scores_gemma":[0.9972597,0.00004220065,0.0001119735,0.002509526,0.00002875502,0.00004783505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009078225,0.0005586401,0.00007291542,0.0001032183,0.00001470365,0.00002543382,0.000382989,0.007398316,0.7244347,0.01046927,0.2498227,0.006626266],"study_design_scores_gemma":[0.0006176826,0.00004222092,0.00005826314,0.00005264193,0.00001513368,0.00001624773,0.001580181,0.04869705,0.05866076,0.08167274,0.8080413,0.0005457398],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.347383,0.0002465613,0.0352455,0.0009575662,0.0004257272,0.001771301,0.604244,0.000379291,0.009347017],"genre_scores_gemma":[0.5382797,0.00001502661,0.03211189,0.0002069423,0.00006847872,0.0008251446,0.4275656,0.00003366972,0.0008935505],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.665774,"threshold_uncertainty_score":0.999949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04527096868765498,"score_gpt":0.3220505935548303,"score_spread":0.2767796248671754,"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."}}