CORRECTION OF ANALYTICAL RESULTS FOR RECOVERY: DETERMINATION OF PAH<b>s</b>IN AMBIENT AIR, SOIL, AND DIESEL EMISSION CONTROL SAMPLES BY ISOTOPE DILUTION GAS CHROMATOGRAPHY-MASS SPECTROMETRY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Results from soil, diesel emission and ambient air control particulate matter samples on the determination of 30 PAHs were evaluated to establish whether the use of recovery data would result in an improvement of the quantitation accuracy over the uncorrected data. The performance of the recovery-corrected technique was initially evaluated using recovery results from PAH standard reference material samples spiked with analogue deuterated isotopes. The results showed an excellent correlation between recoveries of natives and corresponding surrogates for all matrices studied. The practical merit of the isotope dilution mass spectrometry technique was further assessed by spiking control samples with corresponding isotopic analogues and comparing the measured concentration of natives obtained with uncorrected and recovery-corrected techniques. The data revealed that the use of recovery correction leads to results closer to the real values, thus decreasing the negative bias due to losses that occur during the analytical process. The mean accuracy difference between uncorrected and corrected data is more pronounced as the sample matrix becomes more complex, such as soil (15 ± 12%) or diesel emission (8 ± 11%), and less for simpler ambient air matrix samples (3 ± 16%). Precision between the two techniques was comparable within each matrix and relatively close between the different matrices. Keywords: PAHs quantitationisotope dilution mass spectrometryrecovery-correctionGC-MS environmental analysis The authors would like to thank Dr. Ewa Dabek, of Environment Canada, for her valuable comments and technical revision of this manuscript.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it