Bibliographic record
Abstract
Reading the letters about green power in the June 2004 issue of Physics Today, (page 11), I was reminded of an observation I made some years ago in the Bay Area of San Francisco and in Los Angeles; there appear to be almost no solar water heaters on the roofs of buildings there. Many parts of California enjoy essentially the same sunny weather as southern European countries such as Greece, where individual solar water heaters can be seen on the roofs of almost all buildings. Consisting essentially of a small black water-storage tank, such solar collectors are efficient water heaters that offer a low-cost supplement (not replacement) to more conventional technologies. Why are they not used in the sunnier parts of the US? If they were, the financial savings to each household would be great, and the combined energy savings across the US would be enormous.Local energy-saving solutions have enormous potential to reduce our dependency on fossil fuels. A full account of the economics of solar water heating is contained in the publication A Consumer’s Guide: Heat Your Water with the Sun, available from the US Department of Energy (www.nrel.gov/docs/fy04osti/34279.pdf). According to that document, for homes with electric water heaters, up to 25% of domestic energy costs go to heating water. The adoption of local energy-saving solutions should be considered wherever practical and built into new homes. The nature of the solutions depends on the location; excellent insulation and good use of sunlight should be high on everybody’s list.© 2005 American Institute of Physics.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.064 | 0.027 |
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".