Projecting vector-based road networks with a shortest path algorithm
Bibliographic record
Abstract
Manually designing road networks for planning purposes is labour-intensive. As an alternative, we have developed a computer algorithm to generate road networks under a variety of assumptions related to road design standards. This method does not create an optimized road network, but rather mimics the procedure a professional might use when projecting roads by hand. Because many feasible road networks are possible, sensitivity analysis is required to choose the best ones. Such analysis gives forest planners additional information with which to assess the long-term consequences of road density and road standards common in forest management decisions. The procedures used to create road networks are presented in this paper, along with a sensitivity analysis of assumptions on total network length, percentage of landings connected, grades, and horizontal and vertical alignment for a case study. We also include a sensitivity analysis of spatial detail such as node density and link characteristics. Although the road network generation algorithm requires manipulation of many input parameters to create desired road networks, and variation between outputs is a concern, the method still offers considerable improvement over manual methods, especially for applications in strategic planning, and appears to be suitable for all types of topography and road standards.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".