A comparison of residual forest following fires and harvesting in boreal forests in Quebec, Canada
Bibliographic record
Abstract
Residual forests are a key component of post-burned areas creating structure within burns and providing habitat and seed sources. Yet, despite their importance to biodiversity and ecosystem processes there is little information on how similar or different residuals in burned landscape are to harvested landscapes. Our goal was to examine and compare the density, size, shape, and spatial arrangement of residual forest vegetation after fire and clearcutting. We evaluated residual forest in two locations within the boreal mixedwood region of Quebec, Canada using aerial photo interpretation and ArcGIS 9.1 software. We found residual stands to be larger and more abundant in harvested zones relative to sites affected by fire. Differences with respect to shape and spatial arrangement of residual forest were also observed among disturbance types. Factors such as proximity to watercourses, watercourse shape, and physiography affected residual abundance and spatial distribution. Residual forest in harvested zones tended to be more elongated with greater edge due to rules governing forest operations. Despite greater quantity of residual forest in harvested areas than fires, managers should still be prudent as the surrounding forest matrix is reduced in many managed landscapes.
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 | 0.002 |
| 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) | 0.002 | 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".