Nature-based solutions in the solid waste management framework in San Carlos City, Philippines
Bibliographic record
Abstract
Abstract Solid waste management (SWM) in many cities in the Philippines remains an escalating challenge, even after the ‘Ecological Solid Waste Management Act of 2000’ or Republic Act (RA) 9003 came into effect. The San Carlos City is one of the local government units in the Negros Occidental province, which is committed to its solid waste management plan (SWMP) and RA 9003. The city successfully surpassed the minimum requirement of a 25% municipal solid waste diversion rate from landfills by utilizing low-cost technology and community involvement. This study utilizes the awareness, action, and advancement framework on the top priority areas of SWM in San Carlos City: the information, education, and communication and the SWMP including the eco-center. The community's awareness saturation is crucial during the initial stages of the law implementation. Action and advancement analysis reveals that the SWMP should adopt innovative nature-based solutions through scaling up the windrow composting and vermicomposting facilities and landfill gas recovery. Other potential green technologies such as fermentation, anaerobic digestion, and bioremediation transform organic waste into valuable resources. The city's environmental management system should consider green infrastructures inspired by nature-based solutions to enhance its sustainable SWM program.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".