Design and implementation of an efficient model for the transformation and use of organic solid urban waste
Bibliographic record
Abstract
Introduction: This article is the development of the "Design and implementation of an efficient model for the transformation and use of organic solid waste", in the “El Conjunto Residencial de Ontario in Bogotá. (Colombia)”. Problem: The accumulation of solid urban waste by the residential community shows an environmental problem that results in health problems, bad odors, the attraction of rodents and a poor physical appearance in the common areas of this complex, caused by the lack of use of this organic waste. Objective: formulate an efficient model for the transformation and use of organic solid waste in the “Conjunto Residencial Tejar de Ontario”. Methodology: Characterization by the method of quartering and use of organic waste through the design of a micro composting plant. Results: Physical properties such as color, odor and texture are recognized as well as chemical parameters such as C / N, pH and humidity, with these tools we obtain a good quality and fertile compost. Conclusion: The transformation of urban organic solid waste is an effective way of mitigating the environmental impact caused by not using it and simultaneously preventing this waste from reaching landfills. Originality: This transformation and use design was carried out for the first time in the “Conjunto Residencial Tejar de Ontario”, it also contains management strategies that allow optimizing the composting operation. Limitations: Find tools that are easily understood by the community to strengthen knowledge of recycling, source separation and composting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".