Chemical Attributes of a Dystrophic Red Latosol of Brazilian Cerrado After Poultry Litter Application Associated to a Urea Inhibitor
Bibliographic record
Abstract
Poultry waste recycling is now a globally practice consolidated in agriculture. However, the proper disposal of poultry waste should enable to reduce N losses and to decrease pollution potential from the high concentrations of poultry litter applied to soil. In this context, the aim of the present study was to evaluate poultry litter doses and the effect of an urease inhibitor on the chemical attributes of a dystrophic Red Latosol. Two experiments were carried out, one in a greenhouse and another in a eucalyptus forest area, both applying completely randomized design with a 2 × 6 + 1 factorial arrangement, applying two types and six doses of poultry litter (5, 10, 20, 40, 80 and 160 t ha-1) plus the controls, with four replicates, total of 52 pots. The application of poultry litter doses altered soil chemical attributes, except for potential acidity. Soil pH, cation exchange capacity (CEC), sum of bases (SB) and electric conductivity (EC) values and Ca, Mg, K, P, S, organic matter (OM), Mn, Zn and Cu levels increased with increasing poultry litter doses, while Fe and B contents decreased. The 40, 80 and 160 t ha-1 poultry litter doses led to contamination potential of the soil due to salinity and alkalinity, and also raised the EC of the percolate at levels limiting to plants. The NBPT (N-butyl thiophosphoric triamide) urease inhibitor exhibited no influence on soil chemical attributes.
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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) | 0.000 | 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".