{"id":"W4246820860","doi":"10.1109/bibe.2008.4696680","title":"Charge state determination of peptide tandem mass spectra using support vector machine (SVM)","year":2008,"lang":"en","type":"article","venue":"","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Université Laval","keywords":"Support vector machine; Mass spectrum; Artificial intelligence; Ion; Spectral line; Classifier (UML); Tandem; Pattern recognition (psychology); Low resolution; Mass spectrometry; Quadrupole ion trap; Computer science; Linear discriminant analysis; Tandem mass spectrometry; Chemistry; Analytical Chemistry (journal); Physics; Ion trap; High resolution; Materials science; Chromatography; Remote sensing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009011251,0.000497341,0.0007064002,0.0009420221,0.0002307248,0.0005087939,0.0004506735,0.0005101857,0.0005431996],"category_scores_gemma":[0.002439372,0.0001397939,0.0003401379,0.0005642007,0.000184108,0.0006517066,0.0002481111,0.000484283,0.0003847872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001842984,"about_ca_system_score_gemma":0.0002692064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005659436,"about_ca_topic_score_gemma":0.0004206077,"domain_scores_codex":[0.9996276,0.00007838955,0.00004358523,0.00008676171,0.000127646,0.00003604033],"domain_scores_gemma":[0.9989096,0.0004501695,0.0001884875,0.00008815782,0.0003247123,0.00003886788],"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":[0.0008831185,0.0002883522,0.02388157,0.0002226713,0.0001311246,0.0002658021,0.00008780618,0.0637827,0.07819796,0.001334421,0.003446652,0.8274779],"study_design_scores_gemma":[0.00002522669,0.0001994023,0.01131913,0.00001974542,0.00003397248,0.0002820758,0.0000637913,0.9559221,0.02920828,0.001818956,0.001080172,0.00002717977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4699305,0.0007209624,0.525514,0.0002070578,0.00008402967,0.00007602164,0.0004080868,0.001843205,0.001216087],"genre_scores_gemma":[0.8872541,0.0001793752,0.1113486,0.00003496612,0.00002401099,0.00003542018,0.0006008495,0.00002045153,0.0005023],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009420221,"threshold_uncertainty_score":0.00476563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02272546064022793,"score_gpt":0.2724715334867817,"score_spread":0.2497460728465537,"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."}}