Bibliographic record
Abstract
Although considerable knowledge has been built-up over the years, the design of various hydroelectric plants, particularly pumped-storage plants, has not always made full use of this knowledge. Mathematical instabilities, which could be resolved by experienced experts, and real physical unavoidable instabilities, make software application by experts not educated and experienced in subject extremely dangerous: bad things can happen if one can't distinguish inaccurate unrealistic results from reasonable good ones. Additionally, in a number of cases there is considerable evidence that already published guidelines, recommendations and standards have not be consistently applied. As a consequence, vital parameters relating to overall safety and sustainable operation which could have been based on the experience and knowledge of designers and equipment suppliers have been neglected. For instance, contrary to certain common working assumptions, the minimum pressure in a draft tube cannot be any constant pressure to fit the complex flow conditions at the exit of the runner. When such realities are combined with pump-turbine "S" instability there is a further increase in the risk of damages and accidents. Rather, a more realistic set of procedures must be applied. What makes such considerations particularly vital is that pumped-storage plants have such excellent potential for further exploitation as they represent a potentially significant environmentally clean load that may help compensate for power variations in the grid and the need to limit wind power output. Energy storage – if well designed and operated – has a great potential for making the overall system and market more efficient and reliable. It is not an exaggeration to say that pumped storage systems could substitute for up to 50% of nuclear and coal generating plants. They are easily manageable and impressively can be 75 – 85% efficient across the energy storage cycle. This work maintains that some crucial technical areas have lost the benefit of valuable experience and knowledge that has accrued in more than 100 years. The poorly coordinated transfer of practical and theoretical experience appears to be the root cause of this loss. The consequences are an unstable market and investment climate, and a greater frequency of accidents, higher inefficiency and the need for more common troubleshooting; yet many of the same problems which have appeared in recent years can be expected to continue to occur with distressing regularity if appropriate steps are not taken. The required and organized multidisciplinary transfer of experience is a major and challenging task, but one that needs to be undertaken by nonpartisan organisations. It is imperative that decisions to achieve this goal be made now. There is a clear and pressing need to plan, finance and implement a variety of long-term initiatives.
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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 | 0.035 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.127 | 0.128 |
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".