Changes in soil acidity and organic carbon in a sandy typic hapludalf after medium-term pig-slurry and deep-litter application
Bibliographic record
Abstract
Successive applications of liquid swine waste to the soil can increase the contents of total organic carbon and nutrients and change acidity-related soil chemical properties. However, little information is available on the effects of swine waste application in solid form, as of swine deep-litter. The objective of this study was to evaluate alterations of organic carbon and acidity-related properties of a soil after eight years of pig slurry and deep-litter application. In the eighth year of a field experiment established in Braço do Norte, Santa Catarina (SC) on a sandy Typic Hapludalf samples were taken (layers 0-2.5; 2.5-5; 5-10; 10-15; 15-20 and 20-30 cm) from unfertilized plots and plots with pig slurry or deep-litter applications, providing the simple or double rate of N requirement of Zea mays and Avena strigosa in rotation. Soil total organic carbon, water pH, exchangeable Al, Ca and Mg, and cation exchange capacity (CECeffective and CECpH7.0), H+Al, base saturation, and aluminum saturation were measured. The application of pig slurry and deep-litter for eight years increased total organic carbon and CEC in all soil layers. The pig slurry and deep-litter applications reduced active acidity and aluminum saturation and increased base saturation down to a depth of 30 cm. Eight years of pig slurry application did not affect soil acidity.
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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".