{"id":"W2564726982","doi":"10.1093/bib/bbw115","title":"Visualizing and comparing results of different peptide identification methods","year":2016,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of Victoria","funders":"","keywords":"Computer science; Tandem mass spectrometry; Workflow; Database search engine; Computational biology; Complementarity (molecular biology); Fragmentation (computing); Sequence database; Search engine; Data mining; Mass spectrometry; Pattern recognition (psychology); Artificial intelligence; Chemistry; Biology; Information retrieval; Genetics; Chromatography","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":[0.009881661,0.002517969,0.001193407,0.01209915,0.0009519301,0.004437608,0.001518066,0.001242032,0.004654942],"category_scores_gemma":[0.01980832,0.0004269769,0.001195554,0.004353899,0.000791369,0.002937501,0.002355698,0.001277053,0.001474681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009038876,"about_ca_system_score_gemma":0.0009901385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001432096,"about_ca_topic_score_gemma":0.0009443375,"domain_scores_codex":[0.9949346,0.001686166,0.0006404288,0.0007183846,0.001715858,0.000304463],"domain_scores_gemma":[0.9821293,0.01038379,0.001272883,0.001747846,0.003867457,0.0005987672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004475047,0.0005677857,0.02186053,0.003146066,0.0008020077,0.0008905955,0.005175065,0.03922588,0.07895802,0.02379892,0.02685291,0.7942472],"study_design_scores_gemma":[0.000556934,0.001251093,0.0345263,0.00117272,0.0007432422,0.001606639,0.004693986,0.5721357,0.2555205,0.06715472,0.05987659,0.0007615148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1287629,0.003614421,0.803222,0.001635576,0.0005478529,0.0004548674,0.004603506,0.05022901,0.006929791],"genre_scores_gemma":[0.2654184,0.00136921,0.7230995,0.00022078,0.0001742115,0.0003895113,0.003549857,0.004060394,0.001718079],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01209915,"threshold_uncertainty_score":0.05225986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02888227135282484,"score_gpt":0.3417483070402555,"score_spread":0.3128660356874307,"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."}}