Bibliographic record
Abstract
In DC traction power distribution systems, rail bond damages, failures or accidental severing of individual negative feeders are generally undetected by operations as it typically leads to negligible impact on the rolling stock performance. However, a negative return circuit failure increases the overall resistance of the traction power negative return system. This increases the rail-to-earth potential and, consequentially, undesirable DC stray current activity leading to added electrolytic corrosion on affected underground metal structures. Moreover, it may lead to overload on unfaulted negative feeders. Hence, a system that continuously monitors the integrity of the negative distribution feeders is needed so that return circuit failures or inadvertent negative cable severing by construction crews can be promptly detected, located and repaired. This paper describes a negative cable monitoring system that was designed based on a substation PC distributed I/O platform and programmed using LabVIEW. The system samples the negative cable shunt signals via a remote analog input module with filtered differential inputs. The computer individually analyses each negative cable current profile in real time and a supervisory alarm is issued if a negative cable failure is detected. The system was installed at several Toronto Transit Commission streetcar traction power substations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".