Bibliographic record
Abstract
All wood utility poles require an effective maintenance program to ensure safe and reliable service. The end of a wood utility pole's useful life can be attributed to several factors including decay, mechanical damage, weathering and changing design circumstances that require the pole to be modified. Pole life can be extend through an effective preservative treatment and maintenance program, but at some point, all poles will reach a point when they are no longer suitable for their intend use. With the increasing cost of quality wood for use in poles, and the environmental concerns regarding pole disposal and chemical treatment of existing poles, new methods are required to restore and maintain wood poles. A research program was initiated at the University of Manitoba's Civil Engineering Composites Facility to develop a repair and restoration technique for wooden poles using fibre-reinforced polymers (FRP) to extend their useful life. Twenty-seven 3050-mm poles were tested as cantilevers under static loading. The experimental results showed that the repair techniques developed for restoring wood poles were successful in restoring fully the original installation strength. The program also included the development of finite element models used to predict the behavior of FRP-rehabilitated utility poles. Equations were also developed to assist in the design of the FRP-repair.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".