Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the end of the 1950s, the extraction of dissolved gases from an oil sample and the determination of the nature and concentration of these gases have been serving as a means of faults detection. The type and extent of a defect can often be diagnosed from the composition of the gases and the rate at which they are produced. This technique, known as Dissolved Gas Analysis (DGA) for detecting certain categories of faults in oil-filled devices that cannot be readily detected by other conventional methods, remains one of the most widely used today. Although there is general consensus that increasing the concentration of dissolved gas is a precursor of local deterioration of insulation, opinions differ when it comes to interpretation of the symptoms. Consequently, the first step towards improving the accuracy of DGA techniques should be understanding the mechanisms associated with chemical reactions contributing to the generation of fault gases in transformer oils. This article intends to show how the chemical composition of the insulation system may affect the analyses. Some data was also included for further understanding.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it