Characterisation parameter of atmospheric pollutant concentration for external insulation
Bibliographic record
Abstract
Currently, the equivalent salt deposit density (ESDD) is adopted to characterise the pollution degree and many environmental pollution parameters are used to characterise air pollution. However, the relationships between ESDD and environmental pollution parameters are not clear. For the purpose of studying the influence of environmental factors such as haze‐fog, high‐conductivity fog on the insulators contamination and the equivalence between artificial salt fog and natural highly conductive pollutions, a parameter named equivalent soluble salt concentration (ESSC) was proposed to measure the soluble salt amount of the atmospheric air in this study. Based on the measuring principle, an ESSC measurement platform was developed. The developed platform was tested in the laboratory and used to measure the ESSC in the field. According to the experimental results, it was found that the soluble components deposited on the specimen surface in the field are the same as that in the atmospheric air nearby. As the pollution deposition is an accumulative process, it is expected that combined with ESDD, the ESSC can be used to predict the ESDD, which would be of significance to study the accumulated mechanism of insulator contamination, as well as to the external insulation design.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".