Intelligent Corrosion Monitoring System for the Management of Existing Steel Transmission Structures
Bibliographic record
Abstract
Abstract One of the largest power transmitters in North America, located in Ontario, completed a multiyear corrosion study to verify the corrosion rate models for zinc and steel. As a result of the initial study which included accelerated aging tests, a long term field corrosion monitoring program was initiated. One hundred and twenty five corrosion monitoring sites (CMS) were selected across the province. The coupons of 100 sites were installed at top and mid height of the towers. These coupons and their supporting racks are grounded to mimic the actual condition of the steel members of transmission towers. The coupons of the remaining 25 sites were installed on the towers at lower elevations (3-5m AGL). These coupons were completely insulated from the tower to study whether electrostatic induction has any effect on corrosion rate or not. To manage this large network of corrosion monitoring sites, the authors have developed an advanced corrosion database management system (ACDMS). This system was enhanced with intelligent software for image analysis using a learning scheme to recognize corrosion type and corrosion severity by analysing digital images of the corroded steel coupons or members. The data from the corrosion monitoring sites are expected to provide our transmission engineers with valuable information to improve the damage assessment, repair, refurbishment, and maintenance of existing steel transmission structures. In addition, this information is expected to help improve the accuracy of corrosion models and the image analysis software.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".