Improved road network design models with the consideration of various link patterns and road design elements
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
The success of an automatic road network layout over steep terrain mainly depends on the model design. Most previous models have used a grid representation that considers only eight adjacent cells when evaluating feasible road links. Here, we present improved models and alignment constraints mapped on a mathematical graph for better designs that are more applicable under field conditions. We have refined the link pattern by considering up to 48 neighbouring cells and have introduced 16 directional classes per grid cell. Optimization techniques, such as shortest path, minimum spanning tree, and Steiner minimum tree algorithms, are used on the graph to derive a road network that is optimal in terms of its construction costs. These improved models have been applied to different mountainous project areas. Our results show that, by considering various link patterns and alignment constraints, one can determine more appropriate and cost-effective locations for road networks, especially in steep terrain.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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