{"id":"W6907538789","doi":"10.25345/c5h12vk83","title":"MassIVE MSV000096767 - High-Accuracy De Novo Prediction for N- and O-linked Glycopeptides Across Multiple Fragmentation Techniques","year":2025,"lang":"en","type":"dataset","venue":"California Digital Library","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fragmentation (computing); Sequence (biology); Identification (biology); Context (archaeology)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000896827,0.0003156529,0.0002493932,0.0001068972,0.0001858189,0.0005041905,0.0002904409,0.0005590096,0.00004963454],"category_scores_gemma":[0.0005549861,0.0003063123,0.000121871,0.0001284598,0.0001397828,0.00006704423,0.000417497,0.0002189161,0.00001547467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003294179,"about_ca_system_score_gemma":0.0002703117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001446091,"about_ca_topic_score_gemma":0.000006415006,"domain_scores_codex":[0.9984597,0.00004266472,0.000364147,0.0005679179,0.0001729574,0.0003926407],"domain_scores_gemma":[0.9990851,0.0001068032,0.0001577306,0.0004082225,0.00008389082,0.000158307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005355421,0.00008030906,0.0004999907,0.000371764,0.00007671053,0.000008600254,0.000004342785,0.000006153277,0.00360733,0.000005645146,0.9850006,0.00980301],"study_design_scores_gemma":[0.0007958137,0.000275571,0.00009772889,0.0001139775,0.00001982015,0.00001133426,0.00002701952,0.00005943556,0.0377896,0.0002117924,0.9603254,0.0002725071],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02038963,0.0002931528,0.00146237,0.0002127729,0.00008672162,0.001214077,0.9761795,0.00007120997,0.00009058516],"genre_scores_gemma":[0.006429068,0.0006540522,0.001436324,0.0004333165,0.0004062331,0.0005508107,0.9890874,0.00003320363,0.0009695848],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03418227,"threshold_uncertainty_score":0.9999389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00780486554916555,"score_gpt":0.2708091477140328,"score_spread":0.2630042821648673,"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."}}