{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003015113,0.0008447004,0.0009128624,0.001401561,0.0005803168,0.001137004,0.0008702219,0.0007012619,0.0008889575],"category_scores_gemma":[0.003679341,0.0005456419,0.0005739516,0.0008538809,0.000434324,0.00137701,0.001666291,0.001014631,0.0009100439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002552898,"about_ca_system_score_gemma":0.0006297811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006003601,"about_ca_topic_score_gemma":0.001289713,"domain_scores_codex":[0.9987323,0.000337084,0.0001248454,0.0002062319,0.0004753483,0.0001241686],"domain_scores_gemma":[0.9985564,0.0006476893,0.0001905503,0.0001682811,0.0003520896,0.00008485077],"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":[0.0005346393,0.000161362,0.003189267,0.000511671,0.0001358453,0.0003613921,0.0002460964,0.003098046,0.9004612,0.001465639,0.001659135,0.08817558],"study_design_scores_gemma":[0.00006928627,0.0005685445,0.004328906,0.00006774138,0.0001278849,0.001357597,0.00009826344,0.09652624,0.8822294,0.001373678,0.01308132,0.0001711372],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2866798,0.001517301,0.7011362,0.0004071643,0.0001637345,0.0006350971,0.0005520764,0.007023052,0.001885457],"genre_scores_gemma":[0.251866,0.0008184183,0.7436393,0.0002478535,0.00004474898,0.0004713437,0.001034408,0.0004513192,0.00142661],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003015113,"threshold_uncertainty_score":0.01594567,"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."}}