{"id":"W3087185205","doi":"10.1002/jcla.23581","title":"Evaluation of the analytical performance of endocrine analytes using sigma metrics","year":2020,"lang":"en","type":"article","venue":"Journal of Clinical Laboratory Analysis","topic":"Analytical Methods in Pharmaceuticals","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"","keywords":"Analyte; Sigma; Six Sigma; Computer science; Statistics; Chemistry; Mathematics; Chromatography; Physics","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01319169,0.0002115513,0.001771196,0.0003130708,0.00005797563,0.00001842913,0.0008195468,0.0001966102,0.001838535],"category_scores_gemma":[0.04631431,0.0001417501,0.001674776,0.006271032,0.0005070235,0.000212201,0.0001721379,0.0009789589,0.000001656849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009021525,"about_ca_system_score_gemma":0.0008357577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002143245,"about_ca_topic_score_gemma":7.137443e-7,"domain_scores_codex":[0.991193,0.001540872,0.003910414,0.0002939176,0.002819516,0.0002423129],"domain_scores_gemma":[0.9873886,0.003314087,0.003278967,0.0005400793,0.005071296,0.0004070319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005524834,0.0006890394,0.8622262,0.0002715202,0.01387815,0.00001343968,0.00006152951,0.09245448,0.01777091,0.0003265828,0.0001151879,0.01164051],"study_design_scores_gemma":[0.001063694,0.0001362219,0.01138063,0.00006427743,0.04157627,0.000002322242,0.00010347,0.8762782,0.06873355,0.0001109646,0.0003993046,0.0001511413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935455,0.0007242056,0.004480196,0.0004735803,0.0001099519,0.00005035159,0.00004178305,0.000006033561,0.0005683633],"genre_scores_gemma":[0.9919403,0.0001761207,0.007189842,0.0002555115,0.0004110623,5.186814e-7,9.928548e-7,0.0000159725,0.000009705721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8508456,"threshold_uncertainty_score":0.9990739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3438357163375659,"score_gpt":0.5246853516681359,"score_spread":0.18084963533057,"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."}}