Manual tracking for solar parabolic concentrator – For the case of solar injera baking, Ethiopia
Bibliographic record
Abstract
The efficiency of renewable energy equipment is highly reliant on the mechanism for properly capturing the resource and the equipment's performance. For any solar-powered device tracking mechanism is critical; it must be able to follow the pattern of the sun's path. This research created a manual tracking mechanism for a solar-powered steam injera stove. The design of the tracking mechanism tracks the path of the sun seasonally and daily for 5 h, half in the morning and a half in the afternoon, beginning at solar noon, it tracks the sun's path every 10 min. It investigates the relationship between solar intensity and receiver surface temperature. There were two experimental tests, one with one receiver surface data point and the other with three data points. The correlation coefficient of solar intensity with heat temperature on the receiver surface was r = 0.726 at the first data point. And, for the three points where data was collected for one and a half hours in the afternoon, on three-point of the receiver surface lower points, the correlation of solar intensity and average heat temperate of those three points is r = 0.766. For both of the experiment, the relationship of the solar intensity pattern shows there is a strong positive correlation with the temperature on the surface of the receiver which indicates the manual tracking system move with the proper path of the sun and concentrates the sun on the point receiver of the solar injera stove.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".