Bibliographic record
Abstract
Distribution network system owners and managers are increasingly more conscious on the significance of their ability to improve the condition and reliability of their network system. A combination of pertinent diagnostic testing are sought after for condition assessment. Adoption of the most appropriate diagnostic system between the one that are already developed and practiced and others that are promising but still in development is not an easy task to achieve. Installations of MVUG in TNB network system basically consist of PILC, XLPE, and mixed (PILC and XLPE) cables. The PILC cables were the first to be introduced. The XLPE cables came in the late 1980s. Currently only XLPE cables are used for new installation but limited length of PILC cables are still kept for maintenance purposes. The voltage levels for the underground cable network system are 11 kV, 22 kV, and 33 kV. At these voltage levels, the total lengths as of year 2004 are roughly 289,000 km for PILC cables and 257,000 km for XLPE cables. The performance of PILC cable is important in terms of supply reliability as its total length is still significant in the network. Therefore, monitoring the condition of these cables is considered very important and has to be looked into seriously
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".