Biodiversity and Forest structure one year following the 2016 Fort McMurray (Alberta) fire event
Bibliographic record
Abstract
Exploration practices for oil sands developments in the boreal forest of western Canada create a network of thousands of kilometers of linear features, particularly seismic lines that dissect these forests posing significant environmental challenges. As wildfire is one of the prevalent stand-replacing natural disturbances in the Canadian boreal forest, it is an important driver of environmental change and stand development that may contribute to the mitigation of such linear industrial footprint. Here, we evaluate the short-term cumulative (also known as combined) effects of seismic lines and wildfire on biodiversity and site conditions. One year after the Horse River (Fort McMurray, Alberta) fire event in the spring of 2016, we compared dissected and undisturbed forests in burned and unburned boreal peatlands, assessing changes in overall stand structure and the responses of a variety of organisms. Soil moisture was significantly higher on seismic lines than in the adjacent forest, suggesting why most of the study sites within the fire perimeter showed little evidence of burning at the line in relation to the adjacent forest. Low fire severity on seismic lines seemed an important driver of local species diversity for ants, beetles, spiders, and plants in disturbed peatlands, resulting in similar species composition on seismic lines both within and outside the burned area, but different assemblages in burned and unburned adjacent forests. Our results suggest that fire did not erase seismic lines; rather, wildfire might increase the influence of this footprint on the recovering adjacent forest. Longer term monitoring will be necessary to understand how boreal treed peatlands respond to the cumulative effect of wildfire and linear disturbances.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".