Contrasting Effects of Road Dust and Atmospheric Dust on Carbon Accumulation in Eastern Canadian Ombrotrophic Peatlands
Bibliographic record
Abstract
Abstract Limited research has been conducted on the role of atmospheric dust as a nutrient source for peatlands, and none has studied the effects of localized and intense road dust on peatland carbon (C) accumulation. To compare the effects of dust deposition from these two sources on peatlands, we examined three ombrotrophic peatlands adjacent to unpaved (gravel) roads in eastern Canada. We find that road dust deposition increases the ash content of peat and decreases its stoichiometric ratios of C, nitrogen (N), and phosphorus (P), with the greatest changes occurring at the site receiving the highest road dust deposition. The dust record since 5,500 cal BP, reconstructed using ICP‐MS analysis of lithogenic elements in a peat core, reveals seven dust episodes. The most recent episode, predominantly from road dust, exhibits a flux 19–95 times higher than the background atmospheric dust flux depending on the reference element. In the catotelm layer of the peat core, the atmospheric dust flux exhibits significant positive correlations with C, N, P, and potassium (K) accumulation rates alongside negative correlations with the C:N and C:P ratios. In contrast, the C accumulation rate is lowest closest to the road and increases with distance away from the road as a result of changes in vegetation composition, nutrient availability, and water levels. The different effects of atmospheric dust and road dust suggest that there may be a threshold effect of dust or nutrient inputs, especially P, on peatland C accumulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".