Integrating forensic chemistry into reclamation : distinguishing geogenic and anthropogenic hydrocarbon sources
Bibliographic record
Abstract
Consideration for the suitability of soil for reclamation is conducted by the analysis of samples for relevant contaminants of concern. The detection of chemicals above a regulatory Standard lead to a conclusion that the soil is contaminated and can lead to diversion of soil from useful salvage for reclamation and potentially require remediation, typically disposal. The presence of coal particulates in soil and sediment is common in many areas of western Canada, due to the erosion of material from bedrock. These coal particulates can yield detection of chemicals, notably petroleum hydrocarbons, which can lead to false conclusions regarding contamination. The correct identification of coal inclusions as a portion of the natural composition of soil and sediment can be used to navigate the regulatory framework for consideration of what contamination is, and forensic chemical assessments are a key method for differentiating sources. The most stringent Standards for remediation of typical coal-derived chemicals is via partitioning from soil to groundwater. The assumptions built into the partitioning equations do not operate well for coal, because they were derived from petroleum source contamination releases. Thus, the identification of coal as the source of contaminants of concern in soil and sediment coupled with an understanding of the models built into the remediation Standards can be used to allow for suitable reuse of soil for reclamation, that may have otherwise been considered unsuitable, without the potential for adverse effect. This study helps to differentiate between geogenic and anthropogenic hydrocarbon sources using forensic chemical assessments. The findings highlight the importance of accurate source identification in preventing false contamination conclusions and improving reclamation efforts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".