FROM ROAD CONDITION DATA COLLECTION TO EFFECTIVE MAINTENANCE DECISION MAKING: SASKATCHEWAN HIGHWAYS AND TRANSPORTATION APPROACH
Bibliographic record
Abstract
An effective road maintenance program requires that road authorities strategically target their investment to those roads that provide the most benefits in return. The main goal is to determine maintenance strategies that minimize the long-term costs of preserving the road network in a desired condition. This process begins by obtaining adequate information about the road network being analyzed so that the right decisions can be made at the right time. Saskatchewan Highways and Transportation (SHT) collects various road condition data either by using an automated data collection system or by manually rating the road network. The SHT automated data collection system consists of the longitudinal profiling subsystem, transverse profiling subsystem and digital video distress collection subsystem. Collected road condition data is then post processed and stored in the centralized database to be later analyzed in the SHT Asset Management System that is concerned with optimizing available funding and providing most benefits for the entire road network. The primary goal of this paper is to describe the road condition data collection process in Saskatchewan and how obtained data is then used to derive an effective road maintenance strategy.
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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 | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".