Is it clean or contaminated soil? Using petrogenic versus biogenic GC-FID chromatogram patterns to mathematically resolve false petroleum hydrocarbon detections in clean organic soils: A crude oil–spiked peat microcosm experiment
Bibliographic record
Abstract
The Canadian Council of Ministers of the Environment (CCME) reference method for the Canada-wide standard (CWS) for petroleum hydrocarbon (PHC) in soil provides chemistry analysis standards and guidelines for the management of contaminated sites. However, these methods can coextract natural biogenic organic compounds (BOCs) from organic soils, causing false exceedences of toxicity guidelines. The present 300-d microcosm experiment used CWS PHC tier 1 soil extraction and gas chromatography-flame ionization detector (GC-FID) analysis to develop a new tier 2 mathematical approach to resolving this problem. Carbon fractions F2 (C10-C16), F3 (C16-C34), and F4 (>C34) as well as subfractions F3a (C16-C22) and F3b (C22-C34) were studied in peat and sand spiked once with Federated crude oil. These carbon ranges were also studied in 14 light to heavy crude oils. The F3 range in the clean peat was dominated by F3b, whereas the crude oils had approximately equal F3a and F3b distributions. The F2 was nondetectable in the clean peat but was a significant component in crude oil. The crude oil–spiked peat had elevated F2 and F3a distributions. The BOC-adjusted PHC F3 calculation estimated the true PHC concentrations in the spiked peat. The F2:F3b ratio of less than 0.10 indicated PHC absence in the clean peat, and the ratio of greater than or equal to 0.10 indicated PHC presence in the spiked peat and sand. Validation studies are required to confirm whether this new tier 2 approach is applicable to real-case scenarios. Potential adoption of this approach could minimize unnecessary ecological disruptions of thousands of peatlands throughout Canada while also saving millions of dollars in management costs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".