{"id":"W2106442889","doi":"10.1186/1477-5956-12-18","title":"An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs","year":2014,"lang":"en","type":"article","venue":"Proteome Science","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Speedup; Computer science; Algorithm; Parallel computing; CUDA; Workstation; Thread (computing); Inference; Multi-core processor; Artificial intelligence; Operating system","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":[0.0004447301,0.0002099552,0.000188021,0.00004744247,0.0007087403,0.0002268484,0.0006467038,0.00008666876,0.00004460394],"category_scores_gemma":[0.000156485,0.000194031,0.00003958015,0.0003684613,0.000673528,0.0007507608,0.0002517314,0.0003465585,0.000005757112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079771,"about_ca_system_score_gemma":0.00006688756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004674768,"about_ca_topic_score_gemma":0.00002519778,"domain_scores_codex":[0.9980511,0.00001662926,0.0002485065,0.0007526259,0.0003402392,0.0005909166],"domain_scores_gemma":[0.9988234,0.0000919641,0.0001111235,0.000640117,0.0001194595,0.0002139228],"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":[0.00001419082,0.0000604919,0.00265262,0.00002542139,0.00000249698,7.281038e-7,0.0002355947,0.0001502779,0.9874482,0.0008497087,0.000005709055,0.008554537],"study_design_scores_gemma":[0.0004447363,0.00005835282,0.001491388,0.00002628274,0.000003987464,0.00001190153,0.00009986443,0.2524923,0.7322446,0.01216704,0.0006714034,0.0002881428],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5463371,0.00001420564,0.4526193,0.00006872969,0.00001502628,0.0003792529,0.0001084841,0.0002174431,0.0002405167],"genre_scores_gemma":[0.6941435,0.00001303313,0.3054698,0.00003353184,0.00007548807,0.00015441,0.00002935783,0.00001872085,0.00006216276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2552037,"threshold_uncertainty_score":0.7912353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01364954957485894,"score_gpt":0.2936853969677619,"score_spread":0.280035847392903,"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."}}