Limestone and Silicate Applications by Different Methods to Correct Soil Acidity
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it