Small-mammal abundance differs between pipelines, edges, and interior boreal forest habitat
Bibliographic record
Abstract
Oil and gas development alters boreal forests by creating early-successional habitat and an increased amount of edge. We evaluated which small-mammal species used pipeline rights of way, the influence of vegetation recovery on pipelines, and edge effects in the adjacent forest. Meadow voles (Microtus pennsylvanicus (Ord, 1815)) were the most common species on pipelines, whereas adjacent forest was dominated by southern red-backed voles (Myodes gapperi (Vigors, 1830)), northern red-backed voles (Myodes rutilus (Pallas, 1779)), and North American deer mice (Peromyscus maniculatus (Wagner, 1845)). Deer mouse abundance was greater along pipeline transects with greater vegetation recovery. Within the forest, irrespective of vegetation recovery on pipelines, meadow voles and deer mice increased near edges. Red-backed voles showed a mixed (i.e., neutral or positive) response to edge. Vegetation variables (i.e., canopy type and cover, ground cover, stem counts, and volume of downed woody material) were important predictors of small-mammal abundance in the forest, but they could not fully account for observed edge effects. Altered small-mammal communities on and adjacent to pipelines may have implications for boreal forest management and conservation through potential changes in predator–prey dynamics and boreal food webs; these implications require further study.
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 | 0.001 |
| 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 | 0.001 |
| 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".