Comparison of Ride Improvement for Various Roadway Rehabilitation Methods in Western Canada (Poster)
Bibliographic record
Abstract
The objective of this project is to determine if significant differences in the magnitude of the roughness reduction in each waveband exists between treatments and between agencies. While IRI is an accepted measure of roughness, a more in-depth method was developed to determine the nature of the roughness for each project which was affecting the ride characteristics (and in turn IRI). All pre-construction profile data collection in AB and BC and all post construction data collection in AB was collected by the same high speed inertial profiling platform as part of network PMS data collection programs. The post construction data collection in AB was typically collected the year after construction. Post construction data collection was carried out by BC MoTI Field Services immediately following construction. The post-rehabilitation Short Wavelength energy levels are substantially lower for the BC projects when compared to the Alberta projects. This has been attributed to the BC post-rehabilitation profile measurements being conducted immediately following construction. In contrast, Alberta projects were tested the following year. Different rehabilitation treatments result in different levels of roughness reduction in each waveband of interest. Although rehabilitation treatments applied in Alberta result in differing levels of roughness reduction, in all cases, Alberta treatments result in a more uniform reduction across all wavebands. In contrast, the same rehabilitation treatments applied in BC result in less reduction in the Long and Very Long wavebands. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".