Redesain penampang kabel wiring APP pelanggan TM untuk perbaikan akurasi pengukuran kWh
Bibliographic record
Abstract
Abstract Current Transfomer(CT) accuracy for big customers is very important. Constraints Execution of CT accuracy testing on big customers is quite a lot of customers and scattered, and longtime outage duration. Development of electronic kwh Meter for checking on-site CT customers as a solution to these problems. The onsite CT measurement method is carried out by comparing the primary and secondary of the CT by utilizing the current coil on the electronic kWh Meter. This method has several advantages, lower costs, easy procurement, easy and fast operation, so the duration of outages can be minimized. This method is very supportive in the implementation of the Revenue Assurance program. Keywords: English, , 3-5 words ABSTRAK Akurasi CT (Current Transfomer) pada pelanggan besar sangat penting. Kendala Pelaksanaan pengujian akurasi CT pada pelanggan besar adalah pelanggan cukup banyak dan tersebar, dan waktu pemadaman yang lama. Pengembangan kwh Meter Alat untuk pengecekan CT Pelanggan secara on site sebagai solusi permasalahan tersebut. Metode penugukuran CT onsite dilaksanakan dengan membandingkan sisi primer dan sekunder CT dengan memanfaatkan kumparan arus pada kWh Meter elektronik. Metode ini memiliki beberapa keuntungan, antara lain biaya lebih murah, mudah pengadaan,mudah dan cepat dalam pengoperasiannya sehingga lama pemadaman dapat diminimalkan. Cara ini sangat mendukung dalam pelaksanaan program Revenue Assurance dalam memastikan kebenaran CT yang terpasang. Kata kunci: Electronic Meter, CT Testing
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.030 |
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".