The extent and magnitude of edge effects on woody vegetation in road‐bisected treed peatlands in boreal Alberta, Canada
Bibliographic record
Abstract
Abstract Treed peatlands can exhibit dramatic shifts in woody plant cover when they are bisected by roads, a product of change in the flow of surface and subsurface water; however, the edge effects that roads have on overstory cover remain poorly understood. We examined how road and environmental conditions influence woody cover in treed fens in northeastern Alberta, Canada. We used generalized linear mixed models to explain variation in cover as measured using airborne laser scanning (ALS) data obtained for 48 road‐bisected fens. Over half of the study fens had >10% differences in canopy cover between the upstream and downstream sides. Variation in cover was best explained by a complex interaction between road side, distance, and type, as well as distance to upland forest and open water, in both rich and poor treed fens. Substrate texture (fine vs. coarse) further explained cover in rich fens. Gravel roads appeared to have the most dramatic effect on cover adjacent to roads (0–20 m) in both fen types, with differences persisting beyond 100 m. In fens bisected by gravel and paved roads, differences in cover between road sides tended to be ameliorated within 200 m, except for unimproved roads where changes were more linear. This study demonstrates the complexity of landscape conditions under which roads built through peatlands can cause structural changes in woody cover and the usefulness of ALS data for studying this phenomenon.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".