Biofertigation of Forage With Effluents From a Cattle Slaughterhouse Green Line: Impacts on Physical-Chemical Indicators of Soil Quality and on Production Biomass
Bibliographic record
Abstract
Cattle slaughterhouses are potential causes the environmental impacts, as it require a large volume of water in meat processing, generate large effluents amount, and promote the Cerrado deforestation for animal husbandry. Therefore, we aim was carried out to assess the effects of the soil application of a green line wastewater from a cattle slaughterhouse in the Brachiaria brizantha growth. The M1 and M2 managements did not contain wastewater of slaughterhouse. The wastewater from the 3rd stabilization pond (M3 to M5), from reception box (M6 to M8), and manure (M9 and M10) were used in the biofertigation managements. The physical-chemical indicators levels did not show significant differences (p < 0.05) before soil preparation and after managements. However, biofertigation in the Cerrado soil can provide a mitigation of the leaching of fine soil particles and cations. In addition, maximum nitrogen dose of wastewater provided a higher leaf biomass productivity than commercial nitrogen. Thus, the fertigation with wastewater can reduce the use of water bodies to crops irrigation and the incorporation of new areas with native vegetation to the agricultural production systems.
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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".