{"id":"W6959056778","doi":"10.1021/ac3018229.s001","title":"Reusable Solid-Phase Microextraction\\nCoating for Direct\\nImmersion Whole-Blood Analysis and Extracted Blood Spot Sampling Coupled\\nwith Liquid Chromatography–Tandem Mass Spectrometry and Direct\\nAnalysis in Real Time–Tandem Mass Spectrometry","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mass spectrometry; Detection limit; Dried blood spot; Extraction (chemistry); Liquid chromatography–mass spectrometry; Analytical Chemistry (journal); Matrix (chemical analysis); Sample preparation; Coating","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009094964,0.0005742511,0.001388039,0.001403181,0.0004639922,0.0001263922,0.0002990798,0.0007570295,0.01486944],"category_scores_gemma":[0.001270473,0.0004389446,0.0004853896,0.002531621,0.0001166711,0.0003015103,0.000149604,0.0004634664,0.0000453473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009740506,"about_ca_system_score_gemma":0.00007005651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006015416,"about_ca_topic_score_gemma":0.00008298846,"domain_scores_codex":[0.9963114,0.0003289858,0.0009263958,0.001230039,0.0001518762,0.001051267],"domain_scores_gemma":[0.9966165,0.001908551,0.0005701485,0.0005251506,0.0002080965,0.000171514],"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":[0.0003885089,0.0003776973,0.007575736,0.000102024,0.003328446,0.0000305788,0.00003003044,0.00000148518,0.9865099,0.000004449524,0.0009062412,0.000744947],"study_design_scores_gemma":[0.01022518,0.002014737,0.03181829,0.001752963,0.008274117,0.0001236859,0.0002309193,0.0005246127,0.9394009,0.0001261553,0.003935294,0.001573191],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.912049,0.01152855,0.004162481,0.000853445,0.000213187,0.001659404,0.06687292,0.0004631721,0.002197881],"genre_scores_gemma":[0.967164,0.001004853,0.01969818,0.0001050648,0.0001800574,0.0001430216,0.009990594,0.00009660865,0.001617614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05688232,"threshold_uncertainty_score":0.9998062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0270525187348146,"score_gpt":0.3163272625483348,"score_spread":0.2892747438135201,"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."}}