Decomposition and nutrient release pattern of animal manures biodegraded by fly larvae in Acrisols
Bibliographic record
Abstract
This study aims to assess the decomposition of manure biodegraded by fly larvae and the nutrient mineralization rates to understand the efficiency of the biodegraded manures for further use as soil amendment. A litter bag experiment was carried out over 75 d in an Acrisol in Benin using poultry manure, pig manure, mixture of poultry and sheep manure, mixture of poultry and cow manure, and mixture of poultry and pig manure, biodegraded by Musca domestica larvae. Nutrients content in the manures during the different stages of decomposition was analyzed. The mono-component exponential model Y t = Y 0 × e −kt best described the manure decomposition and nutrients mineralization. The manures decomposed fast in the soil, and their nutrients were released fast in the poultry manure, the mixture of poultry and pig manure, and the mixture of poultry and cow manure. Nutrient mineralization increased in the order of P < N < K or N < P < K. Biodegradation of animal manures by fly larvae produced high-quality organic fertilizer through fast N and P release. This could aid in reducing the quantities of these elements applied as mineral fertilizer by farmers for sustaining agricultural soil productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".