DEVELOPMENT OF AN ISOTOPE-DILUTION GAS CHROMATOGRAPHIC-MASS SPECTROMETRIC METHOD FOR THE ANALYSIS OF POLYCYCLIC AROMATIC COMPOUNDS IN ENVIRONMENTAL MATRICES
Bibliographic record
Abstract
An isotope-dilution GC-MS (GC-IDMS) method for the analysis of polycyclic aromatic hydrocarbons (PAHs) in various environmental matrices, including soils, sediments and an extended application to air particulate, has been developed. This method allows for the quantification of each target analyte against its isotope-labelled analogue as well as for the correction of analyte recovery during sample preparation and analysis. Using isotope-dilution mass spectrometric analysis, the isotope-labelled internal standards can significantly reduce systematic error (bias) from several sources including sample stability prior to analysis, analyte loss during both the extraction procedure and post-extraction sample workup and from the calibration procedure. Sample analysis and quantification was carried out using a multi-point calibration technique with continuing single-point calibration (daily single-point checks of the calibration) in order to assess daily instrumental performance; various other new quality control measures have also been employed. Faster methods of gas chromatographic analysis were evaluated using different types of GC columns, stationary phases and methods of detection. Overall, this method has improved the quality and accuracy of PAH data produced and has significantly reduced the time required for sample preparation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".