Designing a Forest Road Network Using Mixed Integer Programming
Bibliographic record
Abstract
Forest roads are an essential yet costly part of forest management, and optimization methods are important tools for planning road systems to support harvesting. This paper presents a Mixed Integer Programming (MIP) optimization model to design a forest access system consisting of logging roads for trucking and access spurs for skidding. The network designed is hierarchical in the sense that the two transport systems require significantly different road standards, and timber may only be transferred from access spurs to forest roads. All timber must be transported from harvest sites to exit nodes that connect the forest road network to public roads. A dense network of potential connections is formed by overlaying a regular grid onto the forest, and then calculating costs of inter-node connections using GIS topographical data. Feasible arcs thus determined are input to the optimization model. The model minimizes total cost of road construction and maintenance, skidding and whole transportation in forest. It can be used to develop road system alternatives to support the process of planning the total access system. The model performance is explored on a study area in a mountainous region, where a persistent access network for partial harvesting is required. High quality solutions were achieved in reasonable computational time.
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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 | 0.001 |
| 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) | 0.004 | 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".