Bibliographic record
Abstract
This paper summarizes the City of Saskatoon?s work in developing network level performance indicators for urban roadway networks with reporting that could be easily understood by politicians and the general public. Many years have been devoted to attempts at translating surface condition measures in to performance indicators. However, surface condition measures such as the International Roughness Index (IRI) were difficult for politicians and the general public to understand. The surface condition was also very dependent on the time of year that the data was collected. For instance, if collection occurred early in the year, certain roads would invariably be rated in worse condition due to spring thaw effects. This variability makes it difficult to track annual changes in condition of the networks. In fact, many have been observed to improve in condition without any work performed due to this variability. A more reliable evaluation tool was needed which could easily be understood with minimal or no roadway knowledge. With this criteria in mind, various asset valuation techniques were investigated. Many of the current methods such as straight line depreciation were rejected due to its inability to provide managers with the current value of the asset. The historical costs approach to valuation gives very little worth to old infrastructure assets, but is useful for valuing new assets. However, this method is not realistic because road infrastructure assets by their nature have sustainable value long after their intended ?design life?. Ultimately, a new method that uses probability distributions to account for variability in network value due to aging was developed. Treatment history and more important treatment sequencing have a significant affect on the long-term value of the assets.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".