Training T&D's next generation for next generation networks: The CIGRE experience
Bibliographic record
Abstract
The Transmission and Distribution (T&D) industry is facing the greatest set challenges in a generation. The impending retirement of the post War ldquoBaby Boomersrdquo, together with major increases in demand for investment in network infrastructure, covering both the renovation and re-enforcement of networks constructed in the 1960s and 1970s, plus the huge demand growth in developing nations, is being faced by a decline in engineering students ready to grasp the challenge. CIGRE - the ldquoConseil International des Grands Reacuteseaux Electriquesrdquo - set out to reposition itself in order to better serve the needs of new T&D engineers. Quite simply, CIGRE acknowledged the need to change or risk declining membership. The authors present case material from the first 12 months of CIGRE UK's highly successful ldquoNext Generation Networkrdquo (NGN) group and how Industry, Academia and Government have recognized the need to balance formal training with specific industry-wide support programs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".