Large Scale Power Generation: Up-Skilling Welsh Industry
Bibliographic record
Abstract
As the move to a low carbon economy presents new challenges for existing industry, there is increasing consensus on the need to address the green skills agenda in order to ensure that the transition is timely and effective. There is however little empirical evidence of the development and delivery of low carbon training courses, specifically in the area of Large Scale Power Generation (LSPG). This study examined the level and form of existing low carbon combustion training in and around Wales and the demand for such training amongst Welsh industry. This is with a view to developing training courses through the Welsh Energy Sector Training project (WEST). Demand for potential WEST courses was found to be positive; specific interest for course content included: The Nature of Fuels, Utilization of Waste, Energy Conversion Processes, Energy Conversion Technologies, Combustion Science, Improving Combustion Efficiency & Emissions and Combustion Risks & Hazards. Critically, geographical factors must be taken into account when assessing the most appropriate form of delivery; e-learning could be a useful tool for maximizing participant numbers. An important implication from the research arose; existing training is generally provided at master’s degree level, whilst findings indicate a preference for courses at introductory undergraduate degree level. As such a close collaboration with participants is required if the training developed is truly to be of value to industry in Wales during the transition to a low carbon economy.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".