Growth and Yield of Tomato Cultivated on Composted Duck Excreta Enriched Wood Shavings and Source-Separated Municipal Solid Waste
Bibliographic record
Abstract
A greenhouse experiment was conducted to evaluate the use of growth substrates, made with duck excreta enriched wood shaving compost (DMC) and the organic fraction of source-separated municipal solid waste (MSW) compost, on the growth and yield of tomato (Lycopersicum esculentum Mill. cv. Campbell 1327). Substrate A consisted of 3:2 (W/W) proportion of DMC and MSW composts. Substrates B and C were the same as A but contained 15% (W/W ratio) of brick dust and shredded plastic, respectively. Three control substrates consisted of the commercially available peat-based substrate (Pr), an in-house sphagnum peat-based substrate (Gs), and black earth mixed with sandy loam soil (BE/S) in a 1:4 (W/W) ratio. Substrates (A, B, C) and controls received nitrogen (N), phosphate (P) and potassium (K) at equivalent rates of 780 mg/pot, 625 mg/pot, and 625 mg/pot, respectively, or were used without mineral fertilizers. Compared to the controls (Pr, Gs and BE/S), tomato plants grown on A, B, and C produced a greater total number and dry mass of fruits, with no significant differences between them. On average, total plant dry-matter biomass in substrate A, B, and C was 19% lower than that produced on Pr, but 28% greater than biomass obtained for plant grown, on Gs and BE/S. Plant height, stem diameter and chlorophyll concentrations indicate that substrates A, B, and C were particularly suitable for plant growth. Although the presence of excess N in composted substrates favoured vegetative rather than reproductive growth, the continuous supply of nutrients throughout the growing cycle, as well as the high water retention capacity that resulted in a reduced watering by 50%, suggest that substrates A, B, and C were suitable growing mixes, offering environmental and agronomic advantages.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".