Integrated forest harvest planning and road-building model with consideration of economies of scale
Bibliographic record
Abstract
We consider the problem of tactical forest management over a 5-year horizon with yearly periods. The main decisions made consider which harvest areas to cut in each period, the flow of timber from an area to each wood-processing mill to satisfy its annual demand, and which roads to build to access a harvest area not connected to the existing road network. The goal is to minimize the total transportation and road-building costs subject to budget limitations. To explore the benefits of economies of scale (EOS) in road construction, we incorporated this notion in the proposed model. Then, the efficiency of the obtained solution is compared with the model without EOS. The proposed model is a mixed-integer linear program, including several timber assortments and multiple periods. We validated the model for a realistic case in the context of the province of Quebec. The results demonstrate that consideration of EOS significantly reduces the total cost by about 5.3%. In the EOS solution, the road segments that are built every year are very concentrated in specific parts of the region, allowing a road-building company to take advantage of EOS. Moreover, this solution provides a much more efficient timber transportation plan.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".