Spatial optimization of ground-based primary extraction routes using the BestWay decision support system
Bibliographic record
Abstract
Improving productivity in forest logging operations while reducing negative impact on soil and streams has gained increasing attention. Positioning primary extraction routes is crucial in these efforts, as it has a huge impact on efficient and sustainable forwarder passages. To minimize the total forwarding distance and avoid steep terrain and impact on soil and water, we developed a decision support system, including a detailed optimization model and solution method. The main source of information consisted of a detailed digital terrain model, depth-to-water maps, and forest volume density. The information was supplemented with the extent of the stand, position of the landing(s), nature and culture conservation sites, and any known unavoidable crossings in the terrain (e.g., streams). Because fast solution time was a critical requirement, we developed a decomposition method based on Lagrangian relaxation. The system was evaluated in two case studies in Sweden. In the first case, the optimization model performance was analyzed at 30 final harvesting sites. In the second case, experienced forest company staff evaluated the primary extraction routes at 19 harvest sites in operational conditions. The results indicated that the model allowed for faster planning, shorter driving distances, and the potential to reduce negative impact on soil and water.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".