Limestone and Silicate Applications by Different Methods to Correct Soil Acidity
Bibliographic record
Abstract
The study aimed to measure the variation in the values of pH, P, K, Ca, Mg, Al, H+Al, V, and Ca and Mg saturation after limestone and silicate applications as a function of different soil correction methods and incubation periods under a controlled environment. The research was carried out in a greenhouse at the FCA of the Federal University of Grande Dourados (UFGD). The experiment was completely randomized in a factorial scheme (5 × 3 × 5), with four replications. The main factors consisted of five incubation times: 0, 30, 60, 90, and 120 days; three soil classes: dystrophic Red Latosol (LVd), Dystroferric Red Latosol (LVdf), and dystrophic Gray Argisol (PACd); and five soil acidity correction methods: control, Ca/Mg balance for limestone and silicate, and 50% and 70% base saturation. Chemical analysses were performed after each incubation period. A regression analysis was carried out once a significant difference was observed between the means of the main factors of the analysis of variance, being adjusted to quadratic models for pH, P, Al, K, Ca, Mg, H+Al, and V. Statistical analyses were performed in the AgroStat software. The ideal soil incubation time to reach the maximum efficiency of correction of the chemical attributes of LVd, LVdf, and PACd soils by the studied methods ranges from 78 to 86 days. The application of limestone by balance of 60% Ca and 20% Mg and calcium and magnesium silicates achieved the best correction indexes of soil chemical attributes, enabling the proposed equation as a calcium and magnesium silicate calculation.
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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) | 0.001 | 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".