Association of postfire peat accumulation and microtopography in boreal bogs
Bibliographic record
Abstract
Peatlands accumulate organic matter as peat because of disproportionate rates of production and decomposition. However, peat accumulation heterogeneity has not been well studied along the microtopographic gradient (hummocks vs. hollows), particularly with respect to fire. Fire affects peatland species composition by differentially removing vegetation and resetting succession, resulting in peat accumulation changes. We examined peat accumulation and microtopography in two historically burned bogs in Alberta, Canada. Measurements of current and historic microtopography were made, and cores were collected along the gradient to identify depth of peat accumulated since fire, as well as to assess properties of the accumulated peat. Current microtopography is significant and correlated with the immediate postfire surface relief. However, differences in the magnitude of variability between sites suggests that differential rates of growth between features are exacerbated between sites and reflected in bog microtopography. Rates of organic matter accumulation, ranging from 156 to 257 g·m2·year1, were elevated but comparable to published rates of recent accumulation. Organic matter content and accumulation rate were greater for hummocks than hollows at Athabasca bog, but the difference between features diminished at Sinkhole Lake, suggesting that the pattern and properties of peat accumulation and microtopography postfire may be attributable to differences in site conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".