Bibliographic record
Abstract
The companies supplying electric power round the globe are facing various issues related due to the occurrence of fault in the distribution lines. Most of them are investing on the research and development of state-of-art technologies to boost continuous supply of energy to the users. The consumers can be guaranteed of flawless power if it is possible to identify and rectify the faults at the shorter time span than usual. The usual way to identify the fault and fault location is with the aid of man power. This work deals with the design and fabrication of an intelligent system based on the GSM. This system helps in efficient identification of the fault and location of the fault, initiating a message to the respective crew members and the control station and ensures that the technical crew will be able to reach the location very accurately in shorter time and recapitulate power at the earliest. The setup includes a current sensor, Arduino and a GSM module. The system identifies the location of fault and the data regarding the location of fault is efficiently conveyed to the control personnel or monitoring system over GSM. The location of the fault thus obtained is very fine and accurate, and the time needed to identify the location of flaw is greatly reduced.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".