Bibliographic record
Abstract
Animal and poultry manures have been recognized for centuries as organic fertilizers providing essential nutrients to crops and improving soil structure due to their large input of organic matter. Increasing demand for poultry and egg products has led to intensification of the poultry rearing process, resulting in large amounts of poultry manure. Drying can be used to reduce the environmental impact of poultry manure and create a value added product for the farmers (animal feed or organic fertilizer). The aim of this study was to investigate the effect of solar heated air drying on the chemical and biological characteristics of poultry manure and develop a suitable manure solar dryer. The effects of temperature (40-60°C) and manure layer depth (1-3 cm) were evaluated. At the three temperature levels studied, the time required to dry poultry manure in the 1 cm-deep layer was the shortest, followed by the 2 and 3 cm-deep layers, respectively. The optimum depth to dry manure (at which the highest drying effectiveness occurred) was 3 cm. The manure drying rate increased with increasing temperature and was the fastest at 60°C for all manure depths studied. A drying temperature-depth combination of 60°C and 3 cm was the most efficient for the thin layer drying of poultry manure. Drying poultry manure at these conditions provided an effective means of removing moisture and odor and destroying microorganisms, thereby allowing the production of a value added product. This temperature range is feasible with a solar dryer operating in the tropics. A solar dryer operating at a 3 cm manure depth would be capable of drying 336 kg/h. Drying poultry manure with solar heated air resulted in a slight decrease in protein content (from 42 to 41%), a decrease in pH (from 8.4 to 6.6), removal of Salmonellae and a 99% reduction in numbers of total bacteria, yeast, mold and E. coli. Thin layer solar drying of poultry manure proved to be an effective means of converting poultry manure into a value added product while reducing the environmental and health problems that are associated with current disposal options.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".