A multi-objective scenario evaluation model for sustainable forest management using criteria and indicators
Bibliographic record
Abstract
A multi-objective optimization model was created for medium-term forest development planning for an integrated forest products company located in the East Kootenay area of British Columbia, Canada. First, a set of sustainable forest management criteria and indicators were developed based on information that could be collected from regional geographic information sytem (GIS) databases and potential outputs from the model. Next, a new forest development planning unit was created (stewardship unit) in which adjacent forest polygons with similar indicator attributes were aggregated. The planning model was designed to determine appropriate harvest levels and management treatments on each stewardship unit to satisfy objectives determined in a participatory process. The mathematical model uses a fuzzy MAXMIN approach, where each indicator represents an objective in the model. Indicators are valued in the model using targets, thresholds, and triggers (called the 3-T approach). A case study is used to demonstrate the use of the model in a sustainable forest development planning context. The results of the case study show that the planning area is highly sensitive to visual quality, old-growth, and community watershed indicators. The paper concludes with a sensitivity analysis that determines the relative opportunity cost of various sustainable forest management indicators on company profits, employment, and tax revenues.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".