{"id":"W2063486872","doi":"10.1007/s00216-012-6192-3","title":"Peptide sequencing challenge","year":2012,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Nanotechnology; Library science; Materials science","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":[0.0004724898,0.0002559346,0.0002675886,0.00003653879,0.0001095828,0.00005323081,0.0002375719,0.0003456341,0.0002552604],"category_scores_gemma":[0.000508195,0.0002010102,0.000162666,0.0001496503,0.000601936,0.00001132552,0.0003240842,0.0002742458,0.0000788895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000353639,"about_ca_system_score_gemma":0.00009032519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001091674,"about_ca_topic_score_gemma":0.000002721783,"domain_scores_codex":[0.9979675,0.00002678704,0.0003845168,0.0003606191,0.0004275762,0.0008329888],"domain_scores_gemma":[0.9984775,0.00004763078,0.00005033339,0.0003623666,0.0001021667,0.0009600035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007573598,0.002509677,0.07976007,0.002721887,0.002242175,0.00009920562,0.0006303581,0.00001114686,0.7688778,0.00845516,0.05317023,0.08076493],"study_design_scores_gemma":[0.00303453,0.001083704,0.009013918,0.0001680853,0.0006470933,0.0004334185,0.002413932,0.01799422,0.3817786,0.001555685,0.5789392,0.00293763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8865304,0.002675705,0.001303225,0.002864314,0.0001325702,0.0002068008,0.00004366225,0.00005459123,0.1061887],"genre_scores_gemma":[0.992296,0.0008690077,0.0005524596,0.000436819,0.000959832,0.000006238917,0.00007983583,0.00001893199,0.004780845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.525769,"threshold_uncertainty_score":0.819696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02914863576616628,"score_gpt":0.2888318212873978,"score_spread":0.2596831855212315,"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."}}