Bibliographic record
Abstract
The application of optic and photonic technology in electric networks in many cases is subject to constraints that differ from telecommunication or commercial applications. Starting by an overview of the quality of service (QoS) needed, in the first part of this paper we summarise some issues that confronted Hydro-Quebec in applying fibre optic technologies to its network. We explore by presenting lab and field trials some issues related to optical ground wires (OPGW) design and network architecture. We present temperature, vibration, ageing and short circuit current effects. We submit the results and analysis of a first field trial of and OC-48 link over a 265 km OPGW line, PMD measurements and an overview of the final design that is being implemented presently using Raman amplification. In the last section of the paper, we will discuss shortly of non-conventional photonic based technologies, local and distributed sensors and optical phenomenon that are used or have been discovered in utilities optical networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".