Development of Network Level Performance Prediction and a Rolling Capital Planning Job Jar
Bibliographic record
Abstract
This project involved uses strategic and tactical performance prediction modelling software that considers surfaced road condition data (distresses, severities and extents), treatment costs, maintenance costs, treatment life (deterioration curves), allocated budget and maintenance performed on road segments to enable Parkland County, Alberta, Canada, to integrate the road rehabilitation and maintenance programs into an optimized preservation program. The optimized preservation program developed leads to the process of developing Parkland County’s rolling capital road program (called the Job Jar). The paper covers some of the technical and organizational aspects of how this three year asset management project was developed and implemented. The project was successfully completed in 2010 and implemented in 2011. Through applying this process to create the Job Jar, Parkland County’s Engineering Services Department is able to meet Parkland County Council’s Strategic Goal of; “Maintaining high quality infrastructure that will ensure sustainable growth of the County.” The paper discusses: How performance prediction modelling software was used to develop the performance prediction models (both strategic and tactical models) over multiple years, The details of using information from the strategic and tactical modelling to develop a prioritized three year rolling program or the Job Jar, Linking of the Job Jar to a specific strategy for the Parkland County’s surfaced road network, Developing the documented business processes to define the employees' and the organization’s roles, responsibilities and timing of the annual processes to ensure the Job Jar can be presented during Parkland County’s annual budget presentation. Development of Network Level Performance Prediction and a Rolling Capital Planning Job Jar TAC Conference – 2012, Fredericton, NB
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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 | 0.004 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".