Conserving woodland caribou habitat while maintaining timber yield: a graph theory approach
Bibliographic record
Abstract
The fragmentation and loss of old-growth forest has led to the decline of many forest-dwelling species that depend on old-growth forest as habitat. Emblematic of this issue in many areas of the managed boreal forest in Canada is the threatened woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)). We develop a methodology to help determine when and how timber can be harvested to best satisfy both industrial timber supply and woodland caribou habitat requirements. To start, we use least-cost paths based on graph theory to determine the configuration of woodland caribou preferred habitat patches. We then developed a heuristic procedure to schedule timber harvesting based on a trade-off between merchantable wood volume and the remaining amount of habitat and its connectivity during a planning cycle. Our heuristic can attain 84% of the potential woodland caribou habitat that would be available in the absence of harvesting at the end of a 100 year planning horizon. Interestingly, this is more than that which is attained by the current plan (50%) and a harvesting plan that targets high volume stands (32%). Our results indicate that our heuristic procedure (i.e., an ecologically tuned optimization approach) may better direct industrial activities to improve old-growth habitat while maintaining specified timber production levels.
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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".