CONVERSION OF KITCHEN FOOD WASTE TO HALAL ORGANIC FERTILIZERS
Bibliographic record
Abstract
Food produced for human consumption can be wasted up to one-third of the time, causing economic, social, and environmental harm. The value of kitchen food waste is being increasingly recognised, and Brunei Darussalam ranks among the highest in the region, with a solid waste output of 1.4 kg per capita per day. However, just 11.3% of food waste is estimated to have been recycled, with the remainder ending up in landfills. In this context, the purpose of this paper is to provide recommendations for the most environmentally friendly means of disposing of kitchen food waste, with composting providing natural, halal, eco-friendly fertiliser. Thus, a two-month experiment was conducted to produce compost-based fertilizer from kitchen food waste. The nutritional value of the plant was then ascertained by fertilizing one plant of bird’s eye chillies (Capsicum frutescens L) with compost-based fertilizer (CBF) and another plant with clay-based soil (CBS). The results of this study showed that almost all the macronutrients in CBF plants are in the accepted range and show good compost fertilisation. Thus, it demonstrates how composting food waste from households can aid in the management of waste reduction for sustainable and a healthy environment, and nutrient recycling in agriculture.
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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 | 0.001 |
| 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) | 0.002 | 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".