Implementation of the British Columbia Side Road Assessment Plan
Bibliographic record
Abstract
With more than 61,000 lane-km, the British Columbia side road network is an important economic asset to the province, providing access to a large resource-based economy. This network is composed of paved (25,000 lane-km) and unpaved (36,000 lane-km) sections of various geometric and construction standards and low traffic volumes. In the mid-1990s, the British Columbia Ministry of Transportation completed implementation of a comprehensive corporate pavement management application on its entire primary and secondary highway system. The ministry was also committed to extension of the system to all roads under its jurisdiction as part of its asset management practices to support formalized condition assessment and needs analysis processes. One of the obstacles facing the implementation of pavement management application on the side roads was the huge data collection cost, particularly in a time of governmentwide fiscal restraint. The side road data collection project was initiated with the objective of developing a data collection methodology and plan for the entire network by a combination of continuous and sampled approaches. The approach used to modify the existing U.S. Corps of Engineers data collection system for unpaved roads to conditions in British Columbia and the field verification trials that were completed before full-scale implementation are discussed. The data collection blueprint, which combines full and sampled coverage of the network with a road classification system, is also described. The results of the first data collection cycle and lessons learned are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".