{"id":"W2333768537","doi":"10.1021/pr100795z","title":"Modeling Contaminants in AP-MS/MS Experiments","year":2010,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Contamination; False positive paradox; Context (archaeology); Tandem mass spectrometry; Computer science; Identification (biology); False discovery rate; Chromatography; False positives and false negatives; Chemistry; Mass spectrometry; Artificial intelligence; Biology","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":[0.001483114,0.0001052673,0.0002166222,0.0002575099,0.0001223193,0.0000529704,0.0005494698,0.0001616512,0.0003839982],"category_scores_gemma":[0.0002478299,0.00009144194,0.00007194125,0.0002542902,0.0000833475,0.0002085005,0.0001424545,0.002011926,0.0000154987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000131956,"about_ca_system_score_gemma":0.0001665551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009800235,"about_ca_topic_score_gemma":0.0000220333,"domain_scores_codex":[0.9982657,0.00003331063,0.0005160651,0.0001781005,0.0006044221,0.0004023964],"domain_scores_gemma":[0.9988838,0.00006070259,0.0001401625,0.0003276495,0.0004340934,0.0001535217],"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":[0.00006839792,0.0001438893,0.000729211,0.0000340207,0.000007060538,0.00002753744,0.0001416609,0.0000753871,0.9966695,0.0004568057,0.00004538259,0.001601117],"study_design_scores_gemma":[0.0007298419,0.00007363693,0.00006029508,0.0001742713,0.000002158489,0.00007487181,0.0002037719,0.007513861,0.9786476,0.01007532,0.002310913,0.0001334246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870542,0.0001121979,0.007723855,0.0003002209,0.00003050794,0.0003046356,0.000003766632,0.00001513081,0.004455449],"genre_scores_gemma":[0.9559324,0.0001080534,0.04302265,0.000006689875,0.0002423972,0.0001693214,0.000001078573,0.00002521113,0.0004922159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03529879,"threshold_uncertainty_score":0.8740928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08629879655477381,"score_gpt":0.437437666711284,"score_spread":0.3511388701565102,"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."}}