Bibliographic record
Abstract
This article describes best practice bridge maintenance guidelines prepared by the Alberta Department of Transportation for assessments, corrosion control, design, deck rehab, abutments, and footings. Assessments determine the best long-term solution, resulting in maintenance, rehabilitation, or replacement. Factors affecting assessment include condition deficiencies, functional deficiencies, and proposed highway improvements. Corrosion control is achieved through the use of metal culverts. Alternate design is used as a best practice guideline when a bridge is past repair and replacement is the next step. Alternate designs involve an estimated cost difference which is factored according to product development, schedules, cost trends, and aesthetics. Concrete bridge deck rehab is another best practice guideline. Measures that may be selected are based on life-cycle costs. Integral abutments, which are best used on short bridges, aid in eliminating abutment joints and reducing maintenance. Spread footing foundation designs are the last best practice guidelines discussed. While they may be viable alternatives for grade separations, abutments, and land-based piers, their usage may not always be appropriate due to inspection and potential underpinning needs.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.139 | 0.056 |
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".