Alleviating Stray Current Concerns “Our Infrastructure Is Corroding!”
Bibliographic record
Abstract
Abstract While planning or building a brand-new DC transit system with all its new concrete and steel infrastructure, and an expected design life of 75 to 100 years, someone unexpectantly mentions these corrosive words – STRAY CURRENT. Concern sets in: What is stray current, how is it impacting us, and what do we do especially when we have pre-stressed and post tensioned elements in our infrastructure? There are stray current prevention and protection methods available, that when systematically applied throughout the life of your infrastructure, will alleviate these concerns. Prevention of stray current impacts start at the design stage, continue through the construction phase, and become an integral part of a long-range preventative maintenance. Stray current protection is all about building in layers of protection, starting with two key elements: electrical isolation and electrical continuity. The challenge lies in where to isolate and where to apply continuity bonding, and then what testing needs to be performed to verify all is in order. A stray current design guideline along with stray current testing plans need to be developed and implemented. Based on recent experiences on several DC transit projects; this paper will explore the noted key elements of any stray current design and appropriate testing plans required for a successful DC transit system build and long system operating life.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".