Regeneration lags and growth trajectories influence passive seismic line recovery in western North American boreal forests
Bibliographic record
Abstract
Across the western North American boreal region, networks of narrow clearings called seismic lines from oil and gas exploration fragment forests. Restoration of seismic lines for habitat recovery of threatened woodland caribou has been prioritized, but there is little guidance on temporal and spatial targets for boreal forest recovery. Between 2016 and 2022, we sampled regenerating trees on 344 seismic lines with limited re‐disturbance across the oil sands region of Alberta, Canada. We modeled growth relationships for regenerating trees, including regeneration lags, using field and geospatial data to predict passive forest recovery on seismic lines. Recovery on seismic lines in peatland and transitional forests could take >30 years, due to longer regeneration lags (8–13 years) and slower‐growing tree species (>25 years to reach 3 m). Recovery in xeric and mesic uplands was nearly half that, due to shorter regeneration lags (3–5 years), faster‐growing species (9–13 years to reach 3 m), and recent wildfires. Over half of seismic lines in upland forests had predicted regeneration lags ≤5 years, including many seismic lines that burned after initial seismic line clearing, indicating regeneration was not delayed. However, all seismic lines in transitional and peatland forests were predicted to have regeneration lags >8 years. Slower recovery on seismic lines is associated with the compounding effects of longer regeneration lags and slower growth rates of dominant tree species. Restoration efforts should prioritize seismic lines where active treatment can significantly reduce regeneration lags and expedite growth.
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 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 | 0.001 |
| 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".