{"id":"W2409147217","doi":"10.1385/1-59745-026-x:217","title":"Using the Global Proteome Machine for Protein Identification","year":2006,"lang":"en","type":"article","venue":"Humana Press eBooks","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"","keywords":"Proteome; Identification (biology); Interface (matter); Informatics; Computer science; Computational biology; Human proteome project; Proteomics; Bioinformatics; Chemistry; Biology; Engineering; Biochemistry; Operating system","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":[0.00008731268,0.0001209392,0.00009027382,0.000009214577,0.0003318023,0.00007743084,0.0002871173,0.00006708952,0.00001236176],"category_scores_gemma":[0.000007533232,0.00009827341,0.00006523028,0.00001468005,0.00007289441,0.00004103321,0.00006072644,0.00009138846,9.686513e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007981051,"about_ca_system_score_gemma":0.00001468864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004404899,"about_ca_topic_score_gemma":0.00004115354,"domain_scores_codex":[0.9992203,0.000007791867,0.0002439565,0.0002395707,0.000106079,0.0001823628],"domain_scores_gemma":[0.999273,0.000008280237,0.0001739274,0.0004603465,0.00006316065,0.00002130593],"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":[0.00002780715,0.00004376967,0.0000113463,0.0001303965,0.00001422886,4.870446e-7,0.0000231198,0.0001662946,0.5187852,0.4742561,0.0001483071,0.006392963],"study_design_scores_gemma":[0.0002141316,0.000005513079,0.000009333243,0.00002728407,0.00002501841,0.000003679323,0.000004779493,0.003921237,0.7768246,0.1656117,0.05319608,0.0001566786],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04915785,0.0007338333,0.8779941,0.000100618,0.0000253241,0.00377918,0.0003284648,0.0005011084,0.06737952],"genre_scores_gemma":[0.9213591,0.000003208543,0.0640934,0.00002825742,0.0003267202,0.005343198,0.0000765812,0.00004069969,0.008728799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8722013,"threshold_uncertainty_score":0.4007473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05931914504854944,"score_gpt":0.3313004226434186,"score_spread":0.2719812775948691,"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."}}