{"id":"W2313624651","doi":"10.1021/pr300893w","title":"Standardized Protocols for Quality Control of MRM-based Plasma Proteomic Workflows","year":2012,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of Victoria","funders":"Genome British Columbia; University of Victoria; Genome Canada","keywords":"Quality (philosophy); Workflow; Control (management); Computational biology; Computer science; Chemistry; Biology; Database; Artificial intelligence","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":[0.007630609,0.0001902843,0.000638591,0.0002434338,0.0002053331,0.00004702879,0.0006438863,0.0002239209,0.0002250121],"category_scores_gemma":[0.001506818,0.0001553398,0.0003409233,0.0003267957,0.0002364191,0.0002619476,0.00006616733,0.0009454461,0.000004143096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003342785,"about_ca_system_score_gemma":0.0006575782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008096041,"about_ca_topic_score_gemma":6.840071e-7,"domain_scores_codex":[0.9965485,0.0002167804,0.001208363,0.0002031591,0.001107555,0.0007156889],"domain_scores_gemma":[0.9956201,0.0007197572,0.001016602,0.0005338799,0.001811528,0.0002981368],"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":[0.00433071,0.000564425,0.002660224,0.001050422,0.00007634465,0.000001286874,0.00007025794,0.00006983191,0.9842783,0.002022516,0.0003446538,0.004531034],"study_design_scores_gemma":[0.004950255,0.0003987706,0.00008994359,0.0005151097,0.00001749186,0.00001096188,0.00005418547,0.0004026541,0.9678478,0.006508312,0.01902551,0.0001790379],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.380764,0.0001397313,0.5802574,0.001132156,0.00003351753,0.03636254,0.0003342996,0.00005733356,0.0009190452],"genre_scores_gemma":[0.6977299,0.000008873559,0.2661391,0.00001125176,0.0004409754,0.03538652,0.000004434529,0.00005012231,0.0002287776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3169659,"threshold_uncertainty_score":0.6334574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1292667213418021,"score_gpt":0.4811640738525504,"score_spread":0.3518973525107483,"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."}}