{"id":"W2008317045","doi":"10.1021/ac001196o","title":"Implementation and Uses of Automated de Novo Peptide Sequencing by Tandem Mass Spectrometry","year":2001,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":292,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research & Development Corporation","funders":"","keywords":"Chemistry; Tandem mass spectrometry; Sequence database; Mass spectrometry; Database; Isobaric labeling; Computational biology; Database search engine; Peptide mass fingerprinting; Tandem; False positive paradox; Computer science; Search engine; Protein mass spectrometry; Chromatography; Information retrieval; Proteomics; Artificial intelligence; Biochemistry","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":[0.00007142495,0.0001186109,0.0001600459,0.00001462075,0.00005129,0.00001794005,0.0001109293,0.0001064133,0.0006246663],"category_scores_gemma":[0.0000349773,0.0001268635,0.00004525664,0.0001559797,0.00008140098,0.00005256892,0.0000358656,0.0001459901,0.000001423147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001636744,"about_ca_system_score_gemma":0.00004453373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003740848,"about_ca_topic_score_gemma":0.000001519824,"domain_scores_codex":[0.9991794,0.000003300208,0.0002571002,0.0002208526,0.0001148759,0.0002244902],"domain_scores_gemma":[0.9995043,0.00005402742,0.0001012177,0.0001978214,0.00004135763,0.0001012991],"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":[0.00000512626,0.0000181747,0.02398575,0.00006208427,0.00002338655,0.000005299027,0.00001121725,0.00000533839,0.9750816,0.0001779905,0.0004687128,0.0001553542],"study_design_scores_gemma":[0.0002138481,0.000006268848,0.00009665469,0.00002238392,0.00003069121,0.00004775971,0.0003348919,0.003516853,0.9928659,0.001985087,0.0007428749,0.0001368134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700205,0.00005860058,0.01522936,0.0001318587,0.00000127786,0.00004183276,0.00004081704,0.0002218348,0.01425388],"genre_scores_gemma":[0.9825426,0.0001352032,0.01647124,0.00003864384,0.00003749416,0.00001917635,0.00006456403,0.00001640487,0.0006747043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0238891,"threshold_uncertainty_score":0.6839658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148378484794838,"score_gpt":0.318572588763185,"score_spread":0.3037347402837012,"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."}}