{"id":"W2030659385","doi":"10.1142/s0219720006001746","title":"PRIMA: PEPTIDE ROBUST IDENTIFICATION FROM MS/MS SPECTRA","year":2006,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Western University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tandem mass spectrometry; Mascot; Computer science; Identification (biology); Software; Construct (python library); Ion trap; Proteomics; Peptide; Tandem; Artificial intelligence; Mass spectrometry; Data mining; Chemistry; Chromatography; Biology; Engineering","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":[0.00010545,0.0000901074,0.0001562351,0.00006233994,0.00008512779,0.00004038198,0.0001261657,0.00008086247,0.00006742074],"category_scores_gemma":[0.00001360917,0.00007720282,0.00005804296,0.00005805889,0.00007205411,0.0001350024,0.00003028844,0.0001483044,0.000006306123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003691766,"about_ca_system_score_gemma":0.00004220955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002477511,"about_ca_topic_score_gemma":0.000002637095,"domain_scores_codex":[0.9990837,0.000005658583,0.0006452955,0.0000725252,0.00009412131,0.00009872105],"domain_scores_gemma":[0.9989861,0.00009738582,0.0006359479,0.00008254916,0.0001581385,0.00003991816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002941941,0.0008948265,0.0220272,0.0003416601,0.0004673146,0.00001655659,0.0008595681,0.1415984,0.2664633,0.4711695,0.01298384,0.08288368],"study_design_scores_gemma":[0.001182924,0.0001167886,0.01117999,0.00007498171,0.00006829463,0.0002387518,0.0001737018,0.1474957,0.02858127,0.7950354,0.01546662,0.0003855939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2750617,0.0001796145,0.7209714,0.0003949944,0.00003659849,0.00005044919,0.00008414945,0.00002063461,0.003200443],"genre_scores_gemma":[0.5739698,0.00009009969,0.4253263,0.00007925801,0.0002295141,0.000004556749,0.0002303142,0.000006594091,0.00006349784],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3238659,"threshold_uncertainty_score":0.314824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009623334375525457,"score_gpt":0.2478373714365536,"score_spread":0.2382140370610281,"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."}}