Determining optimal road class and road deactivation strategies using dynamic programming
Bibliographic record
Abstract
Forest managers are faced with complicated road construction and deactivation decisions. When construction, upgrading, and deactivation strategies must be determined simultaneously over broad spatial and temporal scales, the problem becomes very complex and decision support systems are needed. In this paper, we report the development and application of an optimal road class and deactivation model using dynamic programming. We tested our model on projected road networks on Hardwicke Island, British Columbia. Sensitivity of inputs such as construction costs, upgrade costs, hauling and maintenance costs, deactivation costs, length of time horizon, discount rate, and haul volume were tested within and between two road networks. Comparison of road networks revealed that haul volume concentration, average haul distance, and total road length are the most important variables that affect road class decisions and total network costs. Within our case study, the road network with the lowest average hauling distance resulted in the lowest total cost (CAN$0.24/m 3 less), because hauling costs are the largest component (46%) of total transportation costs. The dynamic programming model can be used to assess numerous road construction and maintenance assumptions under various silviculture and harvest systems.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".