Approximating natural landscape pattern using aggregated harvest
Bibliographic record
Abstract
Successful implementation of the natural disturbance model for timber harvest is hindered by the lack of strategies to approximate landscape fire pattern. In the forests of Alberta, Canada, the fire regime is dominated by large fires that create large regions of same-aged forest. Current forestry practices disperse harvest blocks across the landscape, causing increased fragmentation as compared with fire. Aggregating harvest blocks is one potential strategy to improve approximation of natural landscape pattern. We used a simulation approach to compare landscape pattern created by aggregated harvest strategies, the current dispersed harvest approach, and the natural disturbance regime for a 270 000 ha forest landscape in northeastern Alberta. Compared with dispersed harvest, aggregated strategies increased compatibility with natural landscape pattern by reducing fragmentation. Capacity to aggregate harvest declined when the constraint of maintaining a constant proportion of deciduous to coniferous harvest was included. We conclude that aggregated harvest can improve implementation of the natural disturbance model by bringing several landscape metrics closer to the conditions that fall within the natural range of variability. Aggregated harvest alone, however, performed poorly at maintaining interior old forest, emphasizing that an explicit old-forest strategy is also required.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".