Divided landbase, overlapping tenures, and fire risk
Bibliographic record
Abstract
The Tardis forest modeling program was used to investigate the effects on timber supply and delivered wood cost of alternative forest tenure policies on a forest management agreement area in northeastern Alberta. Under the current tenure policy (business as usual), the woodlands divisions of one large pulp company and several sawmill companies are responsible for different aspects of planning and forest management on the area. We propose an alternative tenure policy (global planning) whereby one forest management entity is responsible for harvesting timber and delivering it to the various mills. The global planning alternative has several advantages over business as usual, especially for the sawmill companies. With business as usual, the sawmill companies experience shortfalls in timber harvest volume. No shortfall is seen with global planning. Under global planning, delivered wood cost for the sawmill companies is reduced by $2.81 m3 , on average. Forest fire is an important disturbance affecting timber supply in the area. We examine the joint effects of tenure policy and fire using Monte Carlo simulation. The superiority of global planning is even more apparent with fire incorporated in the model. According to our simulations, many sawmills are very likely to experience persistent sharp decreases in delivered wood volume under business as usual. No such decreases occur under global planning. Key words: forest tenure, simulation modeling, timber harvest scheduling, forest fire, policy analysis, boreal mixedwood, sustainable forest management
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".