Bibliographic record
Abstract
The history of wind power is discussed, from pumping water that reclaimed land in the Netherlands in the 1600s to today’s megawatt-scale, grid-tied, electricity-generating behemoths. Installations in Denmark (Vindeby, Copenhagen), the US (West Texas, Wyoming, offshore Atlantic), Spain (100% wind in El Hierro), the UK (London Array, North Sea), and China (China’s Wind Base program is expected to reach 1 terrawatt of grid power by 2050) are examined as are novel horizontal-axis, vertical-axis, and vibrating turbine technologies. The number of onshore and offshore sites continues to increase the amount of grid-tied renewable energy year on year (now 10%). The problems of long-distance transmission, stranded power, and recycling are discussed. The origin of the grid is explained, starting with Nikola Tesla and Westinghouse at Niagara Falls, high-voltage transformers, and central-power plant construction across the globe. Renewable-energy technologies are discussed, including hydroelectric dams, geothermal (Iceland, Italy, the US), and marine energy (Scotland, Canada). The advent of the modern prosumer who buys and sells power to a bi-directional grid, virtual power plants, and microgrids are examined as intermittent renewables require new means to manage distributed resources. The social consequences, reliability, and privacy issues of a growing smart grid are examined.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".