Validation of a Method for the Analysis of PAHs in Bulk Lake Sediments Using GC-MS
Bibliographic record
Abstract
This work presents the validation procedures of an analytical method to determine the 16 PAHs from the US EPA's priority pollutants list in sediment samples using ultrasonic extraction coupled to gas chromatography-mass spectrometry. The extraction techniques are altered by the construction of an extraction flask adapted to the ultrasonic bath that greatly reduces losses and increases extraction efficiency of the volatile compounds, especially naphthalene. Cleanup procedures are also altered to change the polarity of the solvent mixture that contributes to reducing the elution of undesirable compounds. The PAH spiked sediment at 100 microg/kg level shows recovery rate of 68% to 108%. A certified reference material has been analyzed for those compounds showing results conforming to certified values. The optimized procedure is applied to sediment samples from different areas across Southeast Brazil and presents the results from the Ibirité Reservoir (MG, Brazil), a eutrophic water body. The total PAH concentration in these sediment samples varies between 103.96 and 180.87 microg/kg (dry weight). As the detected concentrations are relatively low, the acute toxicity detected in sediment and its pore water is not due to these compounds, but to high concentrations of ammonia, copper, and nickel according to TIE procedures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".