Natural regeneration of forest vegetation on legacy seismic lines in boreal habitats in Alberta’s oil sands region
Bibliographic record
Abstract
Mapping of oil reserves involves the use of seismic lines (linear disturbances) to determine both their location and extent. Conventional clearing techniques for seismic assessment have left a legacy of linear disturbances that cause habitat fragmentation. Little is known, however, about how local and landscape factors affect natural regeneration patterns of trees and shrubs on seismic lines that facilitate mapping and future projections of regeneration patterns. To understand factors affecting early forest regeneration and to predict future trends in regeneration of legacy seismic lines we used LiDAR, forest stand databases and a disturbance inventory of conventional seismic lines to model seismic line regeneration to a 3 m height in a 1806 km2 area in northeastern Alberta, Canada. Regeneration to 3 m was inversely related to terrain wetness, line width, proximity to roads (as a proxy for human use of lines), and the lowland ecosites. Overall, terrain wetness and the presence of fen ecosites had the strongest negative effect on regeneration patterns; the wettest sites failed to recover even after 50 years post-disturbance. Predictions of future regeneration rates on existing lines suggested that approximately one-third of existing linear disturbance footprints in this boreal landscape will remain un-regenerated 50 years later resulting in persistent habitat fragmentation. Model predictions estimating regeneration probability are particularly valuable for estimating current and future forest regeneration trajectories on linear disturbances which are a conservation concern and a focus for restoration and planning by government, industry and conservation organizations.
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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 | 0.001 |
| 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".