Carbon Mineralization in Soils Irrigated with Treated Swine Wastewater
Bibliographic record
Abstract
Treated swine wastewater agricultural use can promote environmental and agronomical improvements, however, the inappropiate management of this organic load added on soil can cause unbalances in soil fertility and in availability of nutrients and/or contaminants. Thus, this study aim was evaluate the organic matter biodegradation of treated swine wastewater (WB) and diluted swine wastewater (WBD) applied in Oxisol clayey texture (CS) and in Ultisol (SS) with medium-sandy texture. The treatments studied were: R1 – CS control; R2 – irrigation with WB on CS; R3 – irrigation with WBD on CS; R4 – SS control; R5 – irrigation with WBD on SS; R6 – irrigation with WBD on SS. Three applications were done in flasks containing 500 g of soils sampled from depth of 0-20 cm, the C-CO2 evolutions and degradation fractions were quantified after each application. The results obtained were adjusted to first-order chemical kinetics model. More than half organic matter was biodegraded between 4 and 10 days of incubation, when higher WB amount was applied (33.3 mm). Sucessive WBD use caused degradation of organic matter remaning of previous application. Higher CO2 evolutions were obtained for Oxisol treatments due to higher carbon contents of this soil. SW use caused depletion of Ultisol native organic matter. However, the WB use in Oxisol provided accumulation of organic matter. Soon, the respirometry test evidenced the importance of evaluate the soil depuration capacity before agricultural use, since that this process can affect the contents of organic matter native of these soils and the availabity of nutrient/contaminant for soil-water-plant system.
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".