{"id":"W2430732513","doi":"10.1016/j.chroma.2016.06.051","title":"A facile and fully automated on-fiber derivatization protocol for direct analysis of short-chain aliphatic amines using a matrix compatible solid-phase microextraction coating","year":2016,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Derivatization; Solid-phase microextraction; Chemistry; Chromatography; Extraction (chemistry); Reagent; Detection limit; Fiber; Aqueous solution; Sample preparation; Coating; Matrix (chemical analysis); Gas chromatography; Gas chromatography–mass spectrometry; High-performance liquid chromatography; Organic chemistry; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004801601,0.0002270531,0.0007027645,0.0004481665,0.00008985832,0.00004410107,0.0001242131,0.0001185255,0.000177965],"category_scores_gemma":[0.0003488052,0.0001639989,0.0003608269,0.0007870645,0.00008436897,0.0001726872,0.00002382913,0.00008396096,2.348846e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000946317,"about_ca_system_score_gemma":0.00007817661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004737567,"about_ca_topic_score_gemma":0.000002123326,"domain_scores_codex":[0.99801,0.00005915379,0.001121324,0.0002163409,0.0003699393,0.0002232676],"domain_scores_gemma":[0.9975896,0.0005785676,0.001114035,0.000169251,0.0004189829,0.0001295304],"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":[0.0003538385,0.000318961,0.006190045,0.0005725528,0.00155587,0.000006937774,0.000102294,0.0004367982,0.9888434,0.000005534355,0.00007458426,0.00153917],"study_design_scores_gemma":[0.002803461,0.0001662741,0.0007416129,0.001027511,0.001091429,0.00005348692,0.0001128198,0.07358117,0.9197705,0.00006038091,0.0003355125,0.00025584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8669308,0.00002120354,0.1284376,0.00004891786,0.00001667034,0.004265876,0.0000594066,0.00006980867,0.0001497289],"genre_scores_gemma":[0.9219665,0.000004448526,0.07618897,0.00001064234,0.00005817406,0.001690279,0.00001130133,0.00003135748,0.00003833657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07314437,"threshold_uncertainty_score":0.668768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04657607948629292,"score_gpt":0.4106018833143368,"score_spread":0.3640258038280439,"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."}}