Forest road network design using a trade-off analysis between skidding and road construction costs
Bibliographic record
Abstract
Designing forest road networks in a large forest land is a challenging task because many feasible alternatives exist and need to be analyzed. To provide field managers with an analytical tool that can create and analyze alternative road networks, we have developed a road network optimization model. The model formulates a large network problem in which links represent two timber transportation options from evenly distributed timber locations: on-road transportation via new roads and off-road transportation using skidders. A heuristic network algorithm is employed to solve the network problem and identify cost-efficient road networks for timber harvesting under given cost parameters. To demonstrate our model, we applied it to a 4760 ha forest in the upper part of the Mica Creek watershed in Idaho owned by Potlatch Forest Holdings, Inc. The sensitivity analyses were conducted to verify the model’s performance under various cost and volume settings. The model-generated road network was compared with a road network proposed by experienced forest engineers in Potlatch. The sensitivity analyses confirm that the model appropriately responds to changes in input parameters. Comparisons between the model output and the manually designed road network indicate that the model tends to develop a tree-shape road network to evenly cover the entire management area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".