{"id":"W4239646953","doi":"10.1142/18609475730045","title":"AN ITERATIVE ALGORITHM TO QUANTIFY THE FACTORS INFLUENCING PEPTIDE FRAGMENTATION FOR MS/MS SPECTRUM","year":2006,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fragmentation (computing); Algorithm; Computer science; Iterative method","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":[0.0001537689,0.0001387409,0.00008717533,0.00003516787,0.0001647594,0.00009016824,0.0001701804,0.00007347213,0.00003637145],"category_scores_gemma":[0.00004241266,0.00009460899,0.00005725343,0.00006941819,0.00002618901,0.00001052588,0.00003992427,0.00007266868,0.00000876902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001917838,"about_ca_system_score_gemma":0.00002376915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004176046,"about_ca_topic_score_gemma":0.0004643727,"domain_scores_codex":[0.9992517,0.00003551537,0.0002172468,0.0001810504,0.0001100516,0.00020446],"domain_scores_gemma":[0.99954,0.00002724473,0.00008214226,0.0002494736,0.00005491717,0.00004624123],"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":[0.0002247616,0.000284526,0.1142211,0.0001056046,0.0002200362,0.000002307819,0.005643987,0.03120876,0.8108023,0.006611679,0.01893862,0.01173632],"study_design_scores_gemma":[0.0007223941,0.001324486,0.1020672,0.00001604617,0.00003180727,0.000009267756,0.001974383,0.04200692,0.794682,0.0004988259,0.05611345,0.0005532727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7711421,0.000009880519,0.2267961,0.0002715127,0.00007681469,0.0003245801,0.00002473335,0.00002069055,0.001333578],"genre_scores_gemma":[0.9515119,0.000001786025,0.04622833,0.000615525,0.0002349734,0.00002963434,0.0004790347,0.00001827055,0.0008805093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1805678,"threshold_uncertainty_score":0.3858043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008144909553516195,"score_gpt":0.283986874785369,"score_spread":0.2758419652318528,"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."}}