MANAGING VARIABILITY Wind Farm at Deli Farm, Delabole, Cornwall. Photo: Report author
Bibliographic record
Abstract
David Milborrow is an energy consultant with 31 years experience in renewable energy. He was first involved in aerodynamic research at the research laboratories of the Central Electricity Generating Board before moving to their headquarters in 1984. From then until 1992 he was associated with policy development, including plans for some of the UK’s first wind farms. His association with variability issues goes back to 1988, when he managed a study for the CEGB that was one of ten carried out under the auspices of the European Commission. After privatisation-- and becoming an independent consultant in 1992-- he completed further studies on the topic for a number of clients including the Canadian Wind Energy Association, Sustainable Energy Ireland, the Carbon Trust and the UK DTI. He also lectures on the topic at three universities. He retains an interest in aerodynamic and engineering issues and has also carried out a number of studies on the economics of renewable energy sources, and comparisons with those of the thermal sources of electricity generation. He is, or has been, an adviser to a number of bodies
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.185 | 0.028 |
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".