Agronomic Evaluation Study: Use of Caiapônia Shale as Magnesium Supplementation for Soybean (Glycine max) and Maize (Zea mays) Cultivation
Bibliographic record
Abstract
The soil of the cerrado is acidic, with high levels of iron and aluminum, and has low fertility due to weathering, which removes elements such as magnesium. To increase the availability of magnesium, essential for various plant processes, an alternative is the use of agrominerals rich in this element. The objective of this work was the agronomic evaluation of the use of Caiapônia shale as magnesium supplementation for soybean and corn crops. It is characterized as Magnesium Silicate containing 18% Magnesium Oxide (MgO) in its composition, in addition to the minerals Montmorillonite, Albite and Ilite. The product was screened to 0.425 mm in sieve no. 40. The study was conducted at the School of Agronomy of the Federal University of Goiás, located in the municipality of Goiânia, Goiás, in a greenhouse in two soils: Red Latosol (LV) and Yellow Latosol (LA). The treatments of Caiapônia Shale (CS) were increasing doses (0, 50, 100, 200 and 400 kg. ha-1 of MgO), in addition to the reference agrominerals, Dianutri (SiMg) and Ibar (MgO) and Wollastonite, source of Ca, with addition of MgO. The experimental design was completely randomized, with eight treatments conducted in quadruplicate. The variables analyzed were magnesium and calcium content and pH in the soil. The Caiapônia Shale (CS) released Mg+2, increasing the availability of magnesium, especially in LV in the first year of soybean and residual in corn in the second year, while in LA it was efficient in the first year.
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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) | 0.001 | 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".