The Development of a Diagnosis Indicator-Based Assessment Tool and Its Application to Rural Settlements in the Region Montes de Maria in Colombia
Why this work is in the frame
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Bibliographic record
Abstract
One of the most affected regions in Colombia in terms of social conflict, deforestation and loss of biodiversity is the region called Montes de Maria. In view of the current land restitution plan the trend of the environmental degradation is most likely to increase due to a higher demand of natural resources caused by the returning population that was displaced during the conflict. With the objective to develop a simple and quick method to diagnose the inefficient and environmentally unsustainable consumption and management of resources for domestic and agricultural purposes from households in that region, the most inclusive method is approached supported by a literature review. As a result, the indicator-based assessment tool pro.eraa was developed with the help of the reference certification tool Green Villages by IGBC of India, the Technical Advice by the One Planet Development of Wales, the local NBA as guidelines and the SDI's of the SDGs. Pro.eraa consists of a total of 51 indicators in the four resource themes: water, energy, waste and activity. The fourth resource "Activity" was necessary to be added during the process due to the agricultural context of the region. Pro.eraa was validated and pre-tested on two sites (Huamanga and Chalan) in Montes de Maria. The tool serves as a decision-aid tool to support the selection of tailored and effective interventions that benefits efficiency and environmental sustainability in regard to the human well-being of the rural population as well as the local biodiversity. Apart from the design and validation process, the work includes a showcase application and evaluation of a site and instructions for implementation. During the literature review, it was particularly noted that the current state of the art literature lacks adequate indicator-based assessment or certification tools that lay the focus on sustainable rural development.
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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 | 0.001 | 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 it