{"id":"W1984589199","doi":"10.1373/clinchem.2008.115543","title":"Quantification of Urinary Albumin by Using Protein Cleavage and LC-MS/MS","year":2009,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Nutrition, Metabolism and Diabetes","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Chromatography; Chemistry; Albumin; Urine; Detection limit; Calibration curve; Peptide; Mass spectrometry; Liquid chromatography–mass spectrometry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002145689,0.001526845,0.0007933133,0.0024982,0.0004794098,0.000928372,0.0008419296,0.001081254,0.0008733178],"category_scores_gemma":[0.002658628,0.0003810995,0.0007372775,0.001533862,0.0008061308,0.0004988876,0.0006296725,0.0008291222,0.0009427349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000981573,"about_ca_system_score_gemma":0.0009781063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001336756,"about_ca_topic_score_gemma":0.001724901,"domain_scores_codex":[0.9964121,0.0006641311,0.0002552147,0.001262143,0.001209669,0.0001966706],"domain_scores_gemma":[0.9983928,0.0003655934,0.0004531868,0.0001128065,0.0005678942,0.0001076311],"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":[0.0002824307,0.0000947619,0.005322839,0.000273992,0.0001363759,0.0001376271,0.00005577718,0.0004647097,0.9752749,0.0001771172,0.0003563019,0.01742327],"study_design_scores_gemma":[0.00005507205,0.0006453695,0.02299958,0.00006026568,0.0001442716,0.001360218,0.00003082411,0.01361568,0.9547833,0.0004057358,0.005806717,0.00009295932],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5632203,0.01686151,0.4031393,0.0005304668,0.0003440127,0.00105352,0.005832443,0.00405058,0.00496778],"genre_scores_gemma":[0.6009291,0.004665294,0.3838197,0.001281005,0.0001531304,0.001507325,0.003953554,0.0002637917,0.003427095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0024982,"threshold_uncertainty_score":0.01134765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04855705509733694,"score_gpt":0.3740780968629054,"score_spread":0.3255210417655685,"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."}}