Agronomic Efficiency of Biotite in Soybean and Corn Silage Production
Bibliographic record
Abstract
The aim of the present study is to assess the agronomic efficiency and potential of using biotite, a remineralizer, as nutrient source to both soybean (Glycine max (L.) Merrill and maize silage (Zea mays L.). These crops were grown in succession in different soils. The research was conducted due to the importance of adopting alternative sources of fertilizers for agriculture and the relevance of using ground silicate rock powders to maximize plant growth. The purpose was to also register this product in Ministry of Agriculture, Livestock and Supply (MAPA) as a soil remineralizer. Biotite (BE), a silicate rock powder used as soil remineralizer, was provided by Embu Mineral Company, Mogi das Cruzes, São Paulo State, Brazil. BE samples were used for particle size, mineralogical and geochemical analysis in order to assess its classification as a soil remineralizers based on Normative Instruction N. 5/2006, by MAPA. Two experiments, one with each crop, were conducted on a sandy loam texture Yellow Latosol (LA) and a clay Red Latosol (LV) soil. The experiments were zed block design with four replicates. Treatments consisted, witness, biotite (BE) remineralizer at four increasing K2O rates (30, 60, 120 and 240 kg K2O ha-1), KCl at 60 kg K2O ha-1, and FMX remineralizer (fine-graded mica schist from Pedreira Araguaia Mineral Company). Both KCl and FMX were used as reference K2O sources. Yield data have shown K release in the soil and absorption by the test plants resulted in yield increases. Biotite behavior in the soil is similar to that of FMX and in some cases, to that of KCl. Biotite has great potential to be used as potassium source in soybean and maize crops.
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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) | 0.001 | 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".