Water and Sustainable Development: Implementation and Impact of Eco-Enzyme Flushing Program in Green Universities
Bibliographic record
Abstract
Water is one of the essential needs for all living things, and it is crucial to maintain its quality, especially in water bodies near campuses.These water bodies are often utilized by people around the campus and may connect with other water sources.However, due to the high water consumption on campus by students, faculty members, and local communities, the water quality around campus needs to be considered.Meanwhile, eco-enzyme is an affordable product that can help sustain and improve water quality.Therefore, this study examines the implementation and impacts of an eco-enzyme flushing program in Indonesian green universities.Qualitative methods were employed in this study by analyzing experts' speeches from a YouTube video of the eco-enzyme festival attended by 22 universities simultaneously through thematic coding.The program's implementation involves cooperation between campuses and the surrounding communities, believing it can improve water quality, a heightened sense of environmental responsibility, and collaboration between various institutions to achieve sustainable development.Experts from green universities argue that this program's benefits extend to the environment and functional products, primarily for agriculture.One tangible benefit of this program is that the lake around the university has better water quality for use by the local community.Moreover, this program offers a longterm solution in line with principles of environmental preservation, social equity, and economic viability.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".