Bibliographic record
Abstract
This paper describes how a core strategy in the Infrastructure and Transportation business plan is to “plan, develop and manage government-owned infrastructure”. A goal under this core strategy is to “Improve the safety, efficiency and effectiveness of provincial highway infrastructure”. This links to Government Goal 14: Alberta will have a supportive and sustainable infrastructure that promotes growth and enhances quality of life. The performance measures used for the department goal relate to physical condition, functional adequacy and utilization. Condition is recorded as % Good, % Fair and % Poor. It is based on International Roughness Index measurements (IRI). Functional Adequacy is recorded as % Functionally Adequate. This is calculated by subtracting deficiencies from 100 %. Deficiencies are based on roadway width, geometrics, surface type and weight restrictions. Utilization is recorded as % Meeting Targets. It is based on capacity level of service (LOS). Actual results are calculated annually and displayed in the department annual report. Predicted three-year results are shown in the department business plan and are based on anticipated budgets. These predicted results show deterioration. Budget levels necessary to prevent this are given. A dollar value is also shown for the deferred maintenance backlog presently in effect. This paper concentrates on the condition and functional adequacy performance measures used at the business plan level for the provincial highway network, along with accompanying trends, as these two measures drive the majority of work on the existing highway network. The paper describes the health of the highway infrastructure in Alberta, how that health is changing over time and the dollar values required to maintain and improve that health.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".