A rational approach for optimization of road upgrading
Bibliographic record
Abstract
Roads designed and constructed in the 1950s and 1960s far from cover present-day traffic needs and require maintenance and renewal. A common practice of upgrading aged two-lane roads consists of pavement resurfacing, but this practice often proves insufficient at providing a high serviceability level and risks being inefficient. An existing aged and distressed road may need more radical upgrading operations. The optimum upgrading strategy must be envisaged with regards to prevailing operational criteria such as road safety, ride comfort, and serviceability. In this context, a systematic approach to the upgrading issue has been elaborated aiming at introducing all significant factors to the processing algorithm designating the optimal intervention in each case. The proposed model is meant to determine and recommend the appropriate strategy for each part of the road network and provide a high level of service and avoid unnecessary expenses. Within this scope, this rational strategy proceeds to an exhaustive assessment of examined roads with respect to their performance and defines improvement priorities in accordance with an innovative management policy. The model distinguishes four levels of “upgrading” activities, with each adopted with respect to the road condition, traffic features, ride quality, and environmental considerations. The option derived is supposed to provide the best cost–benefit upgrading solution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".