{"id":"W2094122547","doi":"10.1021/ac900315w","title":"One-Calibrant Kinetic Calibration for On-Site Water Sampling with Solid-Phase Microextraction","year":2009,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Solid-phase microextraction; Chemistry; Calibration; Analyte; Sampling (signal processing); Desorption; Analytical Chemistry (journal); Extraction (chemistry); Chromatography; Sample preparation; Detection limit; Mass spectrometry; Gas chromatography–mass spectrometry; Adsorption; Computer science","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002300775,0.0004530374,0.000531035,0.00003677407,0.0001735179,0.0001453066,0.0002798671,0.0003765481,0.001798286],"category_scores_gemma":[0.0002062859,0.000367159,0.0002272723,0.0001542124,0.0001300595,0.0001497916,0.0000397785,0.0005083187,0.00003813347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002298236,"about_ca_system_score_gemma":0.00008919722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003421416,"about_ca_topic_score_gemma":4.284426e-7,"domain_scores_codex":[0.9971717,0.00001338954,0.0006617722,0.000863547,0.0005069735,0.000782623],"domain_scores_gemma":[0.9984205,0.00028309,0.0001278949,0.0005879793,0.0001489931,0.0004314804],"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":[0.0008050645,0.0006785325,0.00005293708,0.000311016,0.0001339299,0.00002022477,0.00004387623,0.0001786835,0.9950777,0.0001472541,0.000211507,0.00233931],"study_design_scores_gemma":[0.00202369,0.000109253,0.00002826561,0.0001618485,0.000245011,0.00003037459,0.00003808293,0.01993778,0.9711532,0.0009395217,0.004745827,0.0005871405],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.554987,0.00004452905,0.4089616,0.003955541,0.00004388731,0.0003150355,0.0001097043,0.0004676726,0.03111509],"genre_scores_gemma":[0.9770458,0.000009050718,0.01648603,0.0006457767,0.000555596,0.00004178502,0.0007152001,0.00006458488,0.004436164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4220589,"threshold_uncertainty_score":0.999878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04560188047690186,"score_gpt":0.3506790613669586,"score_spread":0.3050771808900568,"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."}}