Bibliographic record
Abstract
Successful detection of an incipient electrical failure within the windings of power transformers is based upon the possibility of determining the amount and nature of the gases evolved by the oil when subjected to the impact of a strong electric or thermal stress. The cause of the deficiency can be established by analyzing the gases dissolved in the oil. While under normal operating conditions the amount of gas evolved is minimal and therefore of little importance, a significant increase in the concentration of gaseous decomposition products gives cause for concern. Since a large amount of vulnerable hydrocarbon molecules can also be the source of dissolved gases, the gassing tendency of oil should be taken into consideration. The lack of a reliable procedure capable of establishing a quantitive relationship between laboratory results and the performance of oil in service can influence the interpretation of dissolved gas analysis (DGA). Just as the chemical stability of oil is accepted as an indication of the ability of insulating oils to resist oxidation, the concept of electrochemical stability is introduced as a measure of the capacity of the oil to counteract gas evolution under the impact of electrical stress. Knowledge of the amount of gas generated by a sample of oil, when subjected to the influence of a standard high-voltage field, can improve the interpretation of DGA of that oil. This concept also provides a better understanding of the mechanism by which the high-voltage field negatively affects the chemistry of insulating fluids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".