Energy Conservation and Solar Energy Use for Cooking - Impact of Its Massive Adoption in the Arid Zone of Argentina
Bibliographic record
Abstract
The use of solar energy is essential in transforming today's human environments into tomorrow's sustainable human habitats.This paper presents the positive impacts produced by the adoption of energyefficient cooking equipment -using heat retention box cookers and the solar drum ovens -in the arid zone of Argentina.These solar cooking technologies improve the quality of life for local populations and, at the same time, save energy, time and effort in trying to obtain firewood.They minimize serious health problems associated with greenhouse gas (GHG) emissions control.The study documented in this paper demonstrates that usage of these technologies results in the saving of 65.7% of Liquefied Petroleum Gas (LPG) and 63.8% of Firewood (FW) utilized in Argentina's rural and arid zone.These results indicate that, if 50% of the arid zone population were to adopt these technologies, a reduction of 3.09kg CO 2 of greenhouse gases per capita from 2015 to 2050 is possible.
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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".