Development of a condition assessment model for transmission line in-service wood crossarms
Bibliographic record
Abstract
Wood transmission structures, such as H frames, have been extensively used to support electrical transmission lines throughout Canada. The transmission infrastructure is in general aging, and fungal decay of wood crossarms poses a significant risk of failure under adverse weather conditions. A crossarm failure in the transmission system can result in forced outages and customer disruptions that lead to significant economic losses. This paper presents a condition assessment model to prioritize the replacement of transmission crossarms that are near the end of their service life. The proposed standard involves a visual condition rating system, which is validated by results of full-scale testing of a sample of in-service crossarms. Aerial inspection of transmission lines using the proposed visual rating system is a simple, economical, fast, and effective method of assessment. The proposed approach would ensure a more consistent compliance with the condition-based replacement standard specified in the Canadian (Canadian Standards Association Standard CSA 22.3 No. 1-01) and North American (US National Electric Safety Code 2002 edition) standards.Key words: transmission structure, wood crossarms, decay, condition assessment, full-scale testing, visual rating system, statistical data analysis, bending strength.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".