A Statistical Approach to Assess the Filler Dispersion of Silicone Rubber Composites for HV Outdoor Insulators
Bibliographic record
Abstract
This paper investigates the use of a statistical analysis for assessing the filler dispersion in silicone rubber composites used for high voltage outdoor insulation applications. The study particularly examines the use of the Median Absolute Deviation as a robust measure to evaluate the variability, and thus repeatability, of thermogravimetric analysis test outcomes for different silicone rubber composites at different temperature rise rates. Different Alumina-trihydrate filled silicone rubber composites are examined in the study. The results suggest a correlation between the Median Absolute Deviation and the degree of filler dispersion observed under Scanning Electron Microscopy. This correlation in outcomes is particularly evident at$25\ ^{0}\displaystyle \mathrm{C}/\min$. The composites are further tested using the dry-arc resistance test as per ASTM D495. The variability in the erosion resistance outcomes of the dry-arc resistance test correlate with the Median Absolute Deviation outcomes. These outcomes collectively suggest the potential of the proposed statistical approach as a quick assessment tool to supplement microscopic imaging towards understanding the effect of the degree of filler dispersion on the erosion resistance of silicone rubber for use in high voltage outdoor insulation applications.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".