Modeling Triple Solar Still Production Using Jordan Weather Data and Artificial Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of the study were to assess the sensitivity of the Artificial Mural Networks (ANN) predictions to different combinations of input parameters as well as to determine the minimum amount of inputs necessary to accurately model solar still performance. Satisfactory results for the triple solar still suggest that, with sufficient input data, the ANN method could be extended to predict the performance of other solar still designs in different climate regimes. To accomplish this objective, a study has been performed to determine the effectiveness of triple solar still efficiency (η) using ANNs. The study used the following parameters as an input to the ANN: time, hourly variation of cover glass temperature (Tg), water temperature in the upper basin (Tw1), water temperature in the middle basin (Tw2) and water temperature in the lower basin of the triple basin still (Tw3), distillate volume, ambient temperature (Ta), plate temperature (TP) and hourly solar intensity (Is).
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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 | 0.000 | 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) | 0.000 | 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 it