Polycyclic aromatic hydrocarbons in terrestrial and aquatic environments following wildfire: a review
Bibliographic record
Abstract
Wildfires are a natural landscape disturbance in many climates and forest types, but the cumulative impact of human-caused climate change, historical fire management and suppression, and changing species diversity in forests has led to an increase in the size and (or) severity of wildfires in certain regions across the globe. There are a significant number of research studies on the effects of wildfire on human health, forest ecology, hydrology, and the physical, chemical, and biological properties of soils. However, research on the impact of wildfire on watersheds including toxicity in aquatic organisms, water chemistry, and fluvial sediment quality is less extensive, focusing primarily on water quality indicators such as nutrients and sediment flux. Recent research has shown that wildfires contribute to the environment significant amounts of polycyclic aromatic hydrocarbons (PAHs), which are compounds produced during the incomplete combustion of organic material, and are known to be toxic and mutagenic compounds. The primary objective of this paper is to review the recent literature that pertains to the contamination of surface waters and sediments and source apportionment of wildfire-derived PAHs to determine where research gaps remain. Additional objectives are to assess the use of molecular ratios to apportion PAH sources and, finally, to create a roadmap for future studies in designing and conducting research that seeks to determine sources of wildfire-derived PAHs in water and sediment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".