Bibliographic record
Abstract
Most larger jurisdictions now have sophisticated pavement management systems and have been collecting performance data for a number of years. These data have typically been used to determine the status of the network, to develop short to medium-term work programs, and in some cases to project longer-term performance and associated funding requirements. In many cases, the management system indicates the need for significant increases in funding. This presentation will address some of the issues that come with the realization that the local infrastructure gap is much bigger than one imagined. The situation often indicates that wholesale changes are necessary in the way that municipal budgets are developed and financed. This usually involves the development of innovative communications strategies to crystallize the need and to point the way forward. It is intended to cover the key contributing factors that led to the creation of the gap in the City of Edmonton as well as the exacerbating socio-economic and psychological factors that are at play in most jurisdictions. For example, pavements are one of the few public assets where there is no obvious relationship between the users and the cost of service. Also, human psychology often militates against making the right public financing decisions in different economic conditions. The presentation will illustrate the use of relatively recent advances in mapping technology to facilitate communication of asset management issues to decision-makers.
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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.154 | 0.094 |
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".