{"id":"W2326104976","doi":"10.1021/pr4001256","title":"Combining Percolator with X!Tandem for Accurate and Sensitive Peptide Identification","year":2013,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Tandem; Tandem mass spectrometry; Shotgun proteomics; Set (abstract data type); Computer science; Identification (biology); Tandem repeat; Pattern recognition (psychology); Artificial intelligence; Chemistry; Proteomics; Chromatography; Mass spectrometry; Biology; Materials science","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":[0.000914616,0.0000832734,0.0001596323,0.0001122072,0.0002432707,0.0001409015,0.0001557967,0.00007026392,0.00003864128],"category_scores_gemma":[0.0001975384,0.00006330752,0.00003855383,0.0001464944,0.000125399,0.0003349597,0.00005000051,0.0004782115,0.000005503947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000956733,"about_ca_system_score_gemma":0.0001033106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001355422,"about_ca_topic_score_gemma":0.000001548613,"domain_scores_codex":[0.9989448,0.0000300007,0.000314032,0.0001525348,0.0003229528,0.0002356343],"domain_scores_gemma":[0.9979096,0.0002156293,0.000258081,0.0001625067,0.001332813,0.0001213856],"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":[0.00007672148,0.00003964226,0.0005517899,0.0000847491,0.00002882512,0.000003043048,0.0002070601,0.000006822605,0.9957812,0.0009068187,0.0002799729,0.002033346],"study_design_scores_gemma":[0.00100222,0.0003435768,0.001614022,0.0002970805,0.00001744598,0.0001849884,0.001693228,0.001904762,0.9672472,0.0235365,0.001978043,0.000180919],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367365,0.00005399599,0.06051972,0.001192019,0.000005857638,0.0009350691,0.000007917698,0.00001801424,0.0005309556],"genre_scores_gemma":[0.9334676,0.00007548311,0.06504728,0.00000894736,0.0001102029,0.0004685437,0.000002730703,0.00002158107,0.0007975974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.028534,"threshold_uncertainty_score":0.2581606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05397242721055872,"score_gpt":0.3791531253709921,"score_spread":0.3251806981604333,"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."}}