Optimal Routing of Wide Multi-Modal Energy and Infrastructure Corridors
Bibliographic record
Abstract
A multi-modal corridor accommodates multiple modes of energy and transportation infrastructure within the same right-of-way. The existing literature on corridor routing in raster space often focuses on one mode with no consideration of the width. This is not a realistic assumption, especially if multiple modes are to co-exist within the same wide right-of-way. Moreover, newer routing methods that consider corridor width cannot take into account multi-modality and the arrangement of modes within a corridor. We developed two multi-modal wide-corridor routing methods using raster data. In the first method, the cost rasters of all modes are weighted and aggregated into a single composite on which a wide LCP is found. This wide LCP is then divided among the modes based on the desired arrangement. The second method uses a directed transformed graph in which the weight of each edge is calculated using different layers of cost data based on the edge direction, the desired widths and arrangement of the modes. Comparative analyses using synthetic datasets show the superior performance of the second proposed method in finding a muti-modal corridor in comparison with the first mode, and in finding a single-modal corridor when compared to the existing methods.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".