The development and application of a decision support system for sustainable forest management on the Boreal Plain
Bibliographic record
Abstract
Millar Western Forest Products Ltd. manages a forest in west-central Alberta under a Forest Management Agreement (FMA) with the Government of Alberta. Part of Millar Western's planning process brought researchers together to develop a decision support system (DSS) for forest management planning and monitoring programs. Four modules timber supply, biodiversity, FIRE, and WATER were built to evaluate, with the help of indicators of sustainable forest management, current and future forest conditions predicted from computer simulations of alternative management scenarios. In the first round of assessment four management scenarios, distinct by their level of silviculture intensification and by the spatial clearcut layout pattern, were compared. Such comparison has demonstrated that (1) the current forest management scenario improved moose habitat at the expense of timber supply, (2) all scenarios had similar fire risk, (3) generated increases in peak flow and water yield of selected watersheds, and (4) slightly impoverished forest biodiversity. All scenarios were examined in light of a computer-simulated natural disturbance benchmark. This led to landscape design scenarios to reduce fire risk and balance biodiversity indicators with timber supply objectives, one of which was eventually selected for implementation. The company's monitoring and research program is also highly focused on improving DSS modules and the underlying data, hence its association with the Forest Watershed and Riparian Disturbance (FORWARD) project, which considers the effects of forest management on aquatic ecosystem indicators. Key words: decision support system, ecosystem management, forest management, natural disturbance, indicators, sustainable forest management, adaptive management.
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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 | 0.000 |
| 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) | 0.000 | 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".