Seismic line width and wildfire promote ring growth in regenerating black spruce
Bibliographic record
Abstract
Across western Canada, networks of narrow forest clearings used to delineate underground oil and gas deposits lead to the dissection and fragmentation of forested boreal peatlands. These ‘seismic lines’ compress peatland surfaces, delaying tree regeneration. Wildfire is a dominant driver of succession and subsequently interacts with seismic lines. In forested boreal peatlands, wildfire can promote regeneration, but it can also compound the effects if on seismic lines. While responses in tree density and height on seismic lines have been well studied, radial growth has not. To address this gap, we examined 139 cross-sections of regenerating black spruce and tamarack on seismic lines in forested peatlands to summarize radial growth with and without recent wildfire in the last 25 years. We then elucidate which site and disturbance characteristics most influenced the radial growth of black spruce. Specifically, we compared 18 a priori candidate models of different disturbance and site characteristics, both additive and interactive, hypothesized to influence black spruce growth. The most supported model included the additive effects of wildfire and width of seismic line. Mean annual ring width from 2013 to 2017 in black spruce increased by 12 % (95 % CI 0, 24; p = 0.043) for every 1 m increase in seismic line width and increased by 42 % (95 % CI 3, 96; p = 0.031) if lines had recently burned. Our results demonstrate that radial growth of black spruce increases on recently burned, wider seismic lines in forested peatlands. Restoration of seismic lines will require consideration of multiple growth responses to disturbances especially wildfires which are burning more frequently in western Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".