Development of an economically sustainable and balanced tactical forest management plan: a case study in Quebec
Bibliographic record
Abstract
In Canada, most of the forests are publicly owned and forest products companies depend on timber licenses issued by the provincial governments for their wood supplies. According to the Sustainable Forest Development Act effective in the province of Quebec since April 2013, the government is responsible for harvest area selection and timber allocation to companies. This is a complex tactical planning decision with important impacts on downstream economic activities. Moreover, to avoid high grading of forest resources and to determine a sustainable tactical plan that ensures a stable level of availability, quality, and cost of supply over several years, it is necessary to take these criteria into consideration simultaneously during the planning process. We propose a mixed-integer nonlinear goal-programming formulation while employing Nadir theory as a reliable scaling technique to model this multi-objective planning problem. The model is solved by a linearization approach for a real case in the province of Quebec. The proposed solution method enables us to obtain good-quality solutions for relatively large cases. Results show that the proposed model outperforms conventional cost-minimization planning strategy by ensuring a more balanced use of wood supply and costs for all stakeholders over a longer period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".