Inverted Soil Mounding as a Restoration Approach of Seismic Lines in Boreal Peatlands: Implications on Plant and Arthropod Abundance and Diversity
Bibliographic record
Abstract
In northern Alberta, Canada, much of treed boreal peatlands are fragmented by seismic lines—linear disturbances where trees and shrubs are cleared for the exploration of fossil fuel reserves. Seismic lines have been shown to have slow tree regeneration, likely due to the loss of microtopography during the creation of seismic lines. Inverted soil mounding is one of the treatments commonly applied in Alberta to restore seismic lines and mitigate the use of these corridors by wildlife and humans. We assessed the effects of mounding on understory plants and arthropod assemblages three years after treatment application. We sampled five mounded and five untreated seismic lines and their adjacent treed fens (reference fens). Compared to reference fens, mounded seismic lines showed on average lower bryophyte (6.5% vs. 98.1%) and total understory cover (47.2% vs. 149.8%), ground-dwelling spider abundance (226.0 vs. 383 individuals), richness (87.2 vs. 106.4 species) and diversity (19.0 vs. 24.6 species), rove beetle abundance (35.2 vs. 84.8 individuals), and ant richness (9.0 vs. 12.9 species). In contrast, rove beetle and ground beetle richness (39.0 and 14.5 species, respectively) and diversity (16.8 and 7.8 species, respectively) were higher on mounded seismic lines compared to reference fens (richness: 18.0 and 7.5 species, respectively; diversity: 7.0 and 3.8 species, respectively). This is one of the first studies to assess arthropod responses to restoration efforts in the context of oil and gas disturbances in North America, and our results highlight the need to incorporate multiple taxa when examining the impact of such treatments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".