Development of Zonal-Specific Semivariograms for a Strategic RWIS Network Optimization: Case Study
Bibliographic record
Abstract
This paper presents a study aimed at developing zonal-specific semivariograms for zones with different climates using regionalized random variables for a strategic road weather information system (RWIS) network implementation and optimization in a large region. Zonal semivariograms modeled in this study were explicitly compared with regional semivariograms to demonstrate the (dis)similarity in their underlying spatial structures. Large-scale RWIS location and density optimizations were conducted with two groups of semivariograms developed in terms of their weather characteristics, namely regional and zonal, and were conducted to compare outcomes and illustrate their distinct features. A case study based on the existing RWIS network in Ontario, Canada, was used to show the application of the proposed method. The findings indicate that there are very different spatial autocorrelation patterns between regional and zonal-specific semivariograms, thereby emphasizing the need for a strategic zonal-specific RWIS implementation plan. The results of different planning scenarios for optimizing RWIS network also reveal that although the optimal locations are insensitive to the underlying spatial structure (i.e., semivariogram) used to optimize the network, the optimal density is found to be very sensitive to such, providing important yet useful decision-making guidance for improved efficiency and effectiveness of overall winter road maintenance programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".