Bibliographic record
Abstract
India is one of few countries of the World which started hydro power development about a century ago almost simultaneously with the developed countries of the World. From a Hydro: Thermal mix of 45: 55 during late sixties, it has today landed to mind boggling proportion of 25: 75. Had we managed it at just reverse proportion of 75: 25 the average cost of generation would have been much less and there would have been no peaking shortage under the same MW installed under the same investment. China one hand has become second largest economy after US leaving behind India & Japan reaching largest hydro installed capacity in the world (2,20,000 MW) after commissioning world's largest hydro plant – three gorges (22,500 MW). Brazil on the other hand has surpassed a developed country like Canada in terms of GDP (2.31 against 1.39 trillion USD) not by higher installed power capacity (only 116 against 130 thousand MW in Canada) but by a higher Hydro content (84% against 61%). Further pumped storage schemes are now becoming more & more relevant with continuous peaking power deficits of 13% (in some parts running to 30%) and solar & wind facing a major challenge of its utilization 24x7. With the present rate of consumption, world is left with app. 200 yrs. of Coal, 75 yrs. of Nuclear resources, 50 yrs. of gas & 25 years of oil whereas hydropower is perennial sources of energy with least carbon foot prints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".