{"id":"W2085887679","doi":"10.1186/1756-0500-7-444","title":"Fit-for-purpose curated database application in mass spectrometry-based targeted protein identification and validation","year":2014,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency; University of Manitoba; Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Database; Annotation; Biomarker discovery; Sequence database; Computational biology; Identification (biology); Protein sequencing; Proteogenomics; Computer science; Proteomics; Bioinformatics; Biology; Genomics; Peptide sequence; Genome; Genetics; Gene","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":[0.001454044,0.0001301415,0.0001415228,0.0002682049,0.000203089,0.0001026438,0.0002613431,0.0001196725,0.00003453281],"category_scores_gemma":[0.001536212,0.0001378855,0.00002967989,0.0006345501,0.0001094596,0.0001968648,0.00005496156,0.000289762,0.00001678516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00012472,"about_ca_system_score_gemma":0.00007582081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001398507,"about_ca_topic_score_gemma":0.00004666417,"domain_scores_codex":[0.9984061,0.0000951417,0.0003372562,0.000521718,0.0003079094,0.0003318479],"domain_scores_gemma":[0.9982957,0.0005664596,0.0001293202,0.000640076,0.0002808659,0.00008756384],"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":[0.0000664871,0.00006907798,0.003012525,0.0002266038,0.000002037014,1.112512e-7,0.00001062429,0.0001357339,0.9875667,0.006326381,0.00002597562,0.002557741],"study_design_scores_gemma":[0.0004677095,0.00002689183,0.0005996448,0.00005223264,0.000003289948,3.275705e-7,0.00001889745,0.0674689,0.9065312,0.02370867,0.0009775617,0.0001446794],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3021086,0.00003982539,0.6960325,0.0003080283,0.000003487041,0.00121305,0.00008783484,0.0001013364,0.0001053323],"genre_scores_gemma":[0.7903708,0.00001498593,0.2048801,0.000006676498,0.00006428907,0.003635396,0.000881286,0.00002674227,0.0001197918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4911525,"threshold_uncertainty_score":0.5622807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08925710930691144,"score_gpt":0.399886880929801,"score_spread":0.3106297716228895,"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."}}