Divided land base and overlapping forest tenure in Alberta, Canada: A simulation study exploring costs of forest policy
Bibliographic record
Abstract
The forest planning environment in Alberta is complicated by multiple forms of forest tenure and by an arbitrary division of the forest into separate softwood and hardwood land bases. The area within and surrounding the Alberta-Pacific Forest Industries Inc. Forest Management Agreement (FMA) area exemplifies the problem, with a large number of independent forest products companies operating in the area. We model 17 sawmill operators and the Alberta-Pacific pulp mill trying to simultaneously satisfy their mill feedstock requirements from a forest.We examined the inefficiencies introduced by this tenure system using Tardis, a computer simulation model incorporating access development, timber harvest, and regeneration. We examined two scenarios: one representing the business-as-usual case where the 18 forest products companies are operating independently, and one where the forest is managed by one company that harvests timber and delivers it to each of the mills.The costs of the present tenure arrangements are, we believe, substantial enough to warrant a thorough re-examination of forest policy and tenure arrangements in Alberta, specifically with respect to land base designation and overlapping tenures. Key words: forest tenure, simulation modelling, timber harvest scheduling, policy analysis
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".