Solar Purifier of the Seawater, to Ensure Food Security in Social Aspects of Low Income, who sit in Coastal Area and Islands of Ecuador
Bibliographic record
Abstract
In our country, lack of water for human consumption in arid coastal areas and especially on the islands, this is critical problem, therefore, the implementation of a system that allows desalinate seawater, improve considerably the quality of life of our citizens.Water is essential for ensure food and improving the quality of life, life without water is impossible, so it is necessary to generate, adapt and disseminate scientific and technological knowledge to allow safe access drinkable water.To contribute to provide solutions to this serious problem, in this project the analysis and synthesis of the current problem of the lack of potable water in coastal and islands areas of our country and we are proposed system model to purify seawater using radiant energy from the sun in a process of evaporation, distillation and condensation.We build a prototype to find respective test for validating research.The analysis was also performed, showing that the project is viable, sustainable and energy efficient.The project is aimed at low-income social sectors of coastal and islands areas of Ecuador, so materials, components and low-cost equipment, used some recycled.A methodology and procedures that enable the application and commercialization of these systems in Ecuador, by enterprises, mainly young engineers also developed.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".