Industry/university research collaborative: a means for strengthening power engineering education programs
Bibliographic record
Abstract
For the past two decades enrollment in power engineering programs has been low in most universities. This area of engineering has been less attractive to the bright students, as it had the reputation of being an old, mature and unexciting area with limited opportunities for innovation. Employment opportunities in the electric power industry have also been limited. This situation appears to be rapidly changing. The restructuring of the electric power industry - a shift from the monopolistic to a competitive structure has introduced new financial and social pressures. The challenge for the industry is discussed. Both the industry and university participants stand to gain from a healthy and meaningful collaborative research partnership. In addition to leading to very good power engineering education programs, such partnerships could produce new technologies of immense value to the industrial partner. Many of the problems the power industry needs to solve today will require the application of several supporting technologies. Universities are in a better position to form a pool of experts in different disciplines and apply them for the solution of power system problems.
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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 | 0.052 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".