City of Calgary's Pavement Management System - Performance Indices Comparison (Poster)
Bibliographic record
Abstract
Pavement Management Systems (PMS) combine engineering and economics to develop cost-effective solutions for pavement maintenance and rehabilitation. To achieve fact based decision making in managing and maintaining the network efficiently, the City has been using PMS since mid 1980’s. PMS measures the performance of the City’s pavement network and predicts future needs, which is used in developing budget needs at targeted level of service. Every year City invests in network level pavement data collection program to monitor functional and structural performance: Visual Condition Index (VCI) – Automated and manual surface distresses; Riding Comfort Index (RCI) – Pavement roughness (IRI); Structural Adequacy Index (SAI) – FWD on Arterial network. The City of Calgary adopts an overall combined index, Pavement Quality Index (PQI) as a performance measure in evaluating the network condition. While PQI gives us an overall picture of network level needs, this represents another level of aggregation and can involve loss of information. However, performance index is looked at independently at project level in prioritising the segments. Hence an attempt is made to compare the network level needs for overall pavement condition index with that of individual performance indicators to better understand the network condition as indicated by individual performance indicators.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".