The effect of the density of candidate roads on solutions in tactical forest planning
Bibliographic record
Abstract
In this paper, we inquire into whether, and by how much, the use in tactical planning of a dense set versus a sparse set of candidate roads can reduce the two major costs (construction and transportation) of forest operations. This inquiry is conducted by using an optimal road location model to generate dense and sparse sets of candidate roads for three different problem instances. These problem instances were then solved using a mixed integer representation of the integrated tactical planning problem. The results show that the use of a dense set versus a sparse set of candidate roads, for all three problem instances, yielded solutions with a mean decrease in transportation and construction costs of 34.34% and 6.94%, respectively. The mean increase in revenue was 1.06%, and the mean increase in the objective function value (revenue minus the total road construction and transportation costs) was 5.62%. In addition, the mapped solutions reveal the spatial attributes of a lower versus a higher cost road network: straighter roads and more efficiently located forks within the road network. These results were obtained to illustrate how reductions in the costs of transportation and road construction can be achieved in tactical planning.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".