A Bibliographic Analysis of Transformer Literature 1990-2000
Bibliographic record
Abstract
This paper presents an analysis of the bibliography on transformers covering the period from 1990 to 2000. It contains all the transformer subjects: a) Transformer design, b) Transformer protection, c) Transformer connections, d) Transformer diagnostics, e) Transformer failures, f) Transient analysis of transformers (overvoltages, overcurrents), g) Modeling and analysis of transformer using FEM (thermal modeling, losses modeling, insulation modeling, windings modeling). Several international journals were investigated including the following: Advances in Electrical and Computer Engineering, Canadian Journal of Electrical and Computer Engineering, COMPEL (The International Journal for Computation and Mathematics in Electrical and Electronic Engineering), Electrical Engineering, Electric Power Components and Systems, Electric Power Systems Research, European Transactions on Electrical Power, IEEE Transactions on Magnetics, IEEE Transactions on Power Delivery, International Journal of Electrical Power and Energy Systems, and IET Generation Transmission & Distribution. Due to the high number of publication in journals, we are not considering publications of conferences and symposia. A total of 700 publications are analyzed in this paper. The research presented in this paper is important because it contains and analyzes the best research papers on transformers coming from many countries all over the world and published in top rated scientific electrical engineering journals.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.120 | 0.163 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".