Emulating boreal forest disturbance dynamics: Can we maintain timber supply, aboriginal land use, and woodland caribou habitat?
Bibliographic record
Abstract
The effects on timber supply incurred by implementing an ecosystem-based management strategy were evaluated in an eastern Canadian boreal forest management unit. Standard linear programming was used to test the effects of four key policy issues: (1) aim for a targeted forest age structure inspired by natural fire regime and forest dynamics (multi-cohort approach), (2) agglomerate harvest blocks in operating areas to reproduce natural disturbance patterns at the landscape scale, (3) maintain cumulated clearcutting and natural disturbance rates inside the historical range of variability, and (4) exclude from harvest areas of potential interest to aboriginal people. The targeted forest age structure was achieved with a minimum reduction of periodic timber supply, but only after 50 years. Compared to a “business-as-usual” scenario, inclusion of the first three policy issues resulted in a 3% to 11% reduction in planned timber supply and a restoration period requiring that 43% to 67% of the productive area be excluded from clearcutting activities for the next 50 years. Such results require that partial cutting not be confined to operating areas eligible for clearcutting. Further exclusion of forest areas of potential interest to aboriginal people resulted in an additional 4% to 10% decrease in planned timber supply. Validation of the coarse filters used in this study (first three policy issues) was done using habitat requirements of woodland caribou (Rangifer tarandus caribou). Almost all scenarios induced a disturbance rate likely to allow a self-sustaining woodland caribou population within 25 years.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".