Organic Carbon Decomposition in Soil Amended With Organic Compost From Slaughterhouse Residues
Bibliographic record
Abstract
Decomposition kinetic of applied compost in soil depends on the decomposition rate coefficient (k), environmental conditions and the interactions with soil. However, studies with the aim of determining k values for different materials rarely consider interactions with soil. The objective of the current study was to estimate k value of an organic compost, considering the interactions with the soil. Samples of soil mixed with compost were incubated in hermetic recipients for 126 days and evolved C-CO2 was quantified. Nonlinear models proposed in the present study were fitted to evolved C-CO2 data. Better fitting was found in a model that divided the soil organic matter in four pools (labile and recalcitrant native soil organic matter; protected and unprotected added organic matter), values of k on both native organic matter pools were multiplied by a constant denominated priming (pr) only in the cases where the compost was added to the soil and the amount of C in the protected pool is limited to the soil protection capacity. Organic compost produced using carcasses, sheepfold residues and slaughterhouse residues presented k value equal to 0.01179 day-1 at 31 oC without water stress. Compost application increased in 9.8% the decomposition of the native soil organic matter.
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".