Performance Improvement of Shallow Solar Pond using Nanoparticles
Bibliographic record
Abstract
Countries are going through shortage of energy source; consequently the futures are looking for alternative source of energy. A very high potential Source of alternative source of energy is solar energy. Solar pond as one way to utilize solar energy and shallow solar pond (SSP) is one type of solar pond. Shallow solar pond can be built easily and at a comparatively low cost over large space, using and storing solar energy on a grand scale. They can’t pollute the air, and coupled with desalting units, they can be used to purify water. Shallow Solar ponds with nanoparticles give a great result. In this work two shallow solar ponds were constructed and installed side by side to study the effect of adding aluminum oxide AL2O3 nanoparticles on the performance of the ponds (one with nanoparticles , while the other one without ). It was found that the performance of the shallow solar pond in general was improved by addition of nanoparticles , with an increase in the temperature of the lower convective zone varies between 2.1oC to 11.3oC, with the maximum increase is obtained when 0.2% concentration of nanoparticles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".