Exploring the utility of different bulking agents for speeding up the composting process of household kitchen waste
Bibliographic record
Abstract
Abstract Household kitchen waste (HKW) is produced in large quantity and its management is difficult due to high moisture content and complex organic matter. Aerobic composting of HKW is an easy, efficient, cost-effective and eco-friendly method. This study is designed to achieve a zero-waste concept and to convert HKW. We optimized the type and size of three different bulking agents to speed up the composting process. The tested bulking agents were fallen leaves, sawdust and fly ash. The results showed a higher and longer thermophilic phase (55 o C) for 11 days in C2. Higher moisture content (69%) and higher organic matter degradation (38.4%) were also observed in C2. The pH range in all compost treatments was 7-8.5, Electrical conductivity range was 1.8–3.55 mS/cm, C/N ratio range was 15.4–18.1, water holding capacity range was 3.25–4.3 g water/g dry sample, total potassium range was 1.52–1.61%, total phosphorous range was 0.83–1.14%. The highest germination index (119.1%) was also obtained in C2. The highest chili height (16.7 cm), greater number of leaves (20), greater shoot fresh weight (4.75 g) and root fresh weight (1.2 g) was obtained in the presence of C2. Similarly, greater water WHC (2.8 g water/g DW), higher porosity (55.49%) and higher aggregate stability (54.14%) of soil was also obtained by C2. This research effectively reduced the maturation time to 32 days and converted kitchen waste into compost (resource). This is a very practical idea for home composting and kitchen gardening to combat food security issues in developing countries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".