A heuristic approach to automated forest road location
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An optimization problem arising when planning forest harvesting operations is the location of new access roads. The new roads must cover several areas to be harvested at minimum cost. This problem is of economical and environmental relevance in the domain of forestry. In this study, the problem is expressed as a P-forest problem in a graph. It consists of determining a set of tree structures in a graph that covers a set of vertices corresponding to harvest areas. The objective is to minimize the sum of construction costs and harvesting costs. In addition to the location of roads, the P-forest problem has several relevant applications, including public transport, electricity transmission, roads, pipelines, and communication networks design. This paper presents a greedy randomized adaptive search procedure (GRASP) to solve this problem. The heuristic was implemented on a decision support system, and computational experiments were conducted on randomly generated and real instances to demonstrate the performance and practical efficiency of the proposed approach. A comparison with manually designed forest road networks in the real instances shows a clear advantage for the proposed method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it