{"id":"W2081337065","doi":"10.1016/j.clinbiochem.2012.12.019","title":"Rapid development of sensitive, high-throughput, quantitative and highly selective mass spectrometric targeted immunoassays for clinically important proteins in human plasma and serum","year":2013,"lang":"en","type":"article","venue":"Clinical Biochemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Montreal Clinical Research Institute","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Immunoassay; Analyte; Chemistry; Chromatography; Selected reaction monitoring; Biology; Antibody; Mass spectrometry; Immunology; Tandem mass spectrometry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006972263,0.0002908481,0.0006380095,0.00007021389,0.0001395986,0.0000278103,0.0001933378,0.0004304337,0.00004357858],"category_scores_gemma":[0.000858774,0.0002856606,0.0001062988,0.0002977046,0.0004007685,0.00008148627,0.0001572721,0.0005167231,0.00000172239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008907959,"about_ca_system_score_gemma":0.0001648272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002527181,"about_ca_topic_score_gemma":0.000003952327,"domain_scores_codex":[0.996847,0.00003035734,0.001819005,0.0007934747,0.0001475183,0.0003626976],"domain_scores_gemma":[0.9975991,0.0008190277,0.0007776371,0.0003743201,0.0002718428,0.0001580988],"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":[0.000143333,0.0003021374,0.007854524,0.0002358285,0.0001043543,0.000002254578,0.00007682259,4.613195e-7,0.9867836,0.0006015155,0.00006474979,0.003830484],"study_design_scores_gemma":[0.001524516,0.0002543232,0.005947221,0.0001138316,0.00002548045,0.000003994313,0.0002297044,0.0002191193,0.9842705,0.00690769,0.0001497466,0.0003539489],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930145,0.0002088959,0.005109244,0.0001850828,0.00001508232,0.0008529745,0.0001534843,0.00007264567,0.0003880885],"genre_scores_gemma":[0.7008977,0.0002009337,0.2982206,0.00002544008,0.00003851165,0.0003972444,0.00009315412,0.00002695921,0.00009946922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2931114,"threshold_uncertainty_score":0.9999595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02425931032496768,"score_gpt":0.3269862238308735,"score_spread":0.3027269135059058,"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."}}