{"id":"W1998725834","doi":"10.1021/pr8009098","title":"Global Quantitative Proteomic Profiling through <sup>18</sup>O-Labeling in Combination with MS/MS Spectra Analysis","year":2009,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Chemistry; Quantitative proteomics; Chromatography; Fragmentation (computing); Isotopic labeling; Mass spectrometry; Shotgun proteomics; Analytical Chemistry (journal); Elution; Proteomics; Computer 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":[0.001913964,0.0002315975,0.0005310728,0.0005084047,0.0002422331,0.000134328,0.000590945,0.0001796886,0.0001351953],"category_scores_gemma":[0.0003055831,0.0001917296,0.0001724634,0.003020028,0.0001575325,0.0005722615,0.00006734898,0.001355972,0.000006182061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008490423,"about_ca_system_score_gemma":0.0003630947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001305531,"about_ca_topic_score_gemma":0.00002147886,"domain_scores_codex":[0.9968708,0.000146918,0.0008432726,0.0004017469,0.001121767,0.0006155249],"domain_scores_gemma":[0.9978026,0.0001232089,0.0005193566,0.0004016474,0.001014091,0.0001390657],"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":[0.003539875,0.003133984,0.0531526,0.0005456898,0.001210275,0.0003736068,0.002077337,0.04902879,0.8012117,0.08080035,0.0001771686,0.004748593],"study_design_scores_gemma":[0.003909151,0.002299759,0.003257055,0.0009451914,0.0002335347,0.0001245438,0.001209405,0.03197261,0.6952437,0.25987,0.0002302837,0.0007048087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8723992,0.0002927553,0.1197173,0.001349887,0.000004252715,0.001273787,0.00001984247,0.00004648151,0.004896522],"genre_scores_gemma":[0.6355991,0.0001927253,0.3638617,0.00001290301,0.0000811951,0.0001574069,0.000009327067,0.00001827397,0.00006726385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2441444,"threshold_uncertainty_score":0.7818508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06288377554472982,"score_gpt":0.4145891823239326,"score_spread":0.3517054067792028,"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."}}