Increasing Levels of Sulfur on Lowland Rice in Different Soil Textures
Bibliographic record
Abstract
A viable alternative to increase productivity in rice cultivation areas has been the investment in macronutrient fertilization. Sulfur (S) is an essential nutrient for plants, as it participates in the formation of several organic molecules, being essential to ensure high yields of rice. The aim of this paper is to analyze the rice crop performance in two types of irrigated floodplain soils under different doses of sulfur fertilization. The experiment was carried out on two lowland soils in Formoso do Araguaia-Tocantins, Brazil, under a randomized block design, in a 2 × 8 factorial arrangement, two soil types and eight sulfur doses, using ammonium sulfate as the source of S (0, 10, 20, 30, 40, 50, 60 and 70 kg ha-1 of S). The doses were applied when the plants were in the V3-V4 development stage in both soils. S provided plant a growth up to 40 kg ha-1, which is the economical feasible dose for plant height in soil 1. With the increase of the dose of S over 60 kg ha-1 in soil 2, there was a decrease in the number of panicles per m2. There was an increase in productivity in soil 2 and reduction in soil 1. The highest grain yield was obtained in soil with higher clay content. The dynamics of sulfur fertilization in floodplain soils requires further studies for a better understanding.
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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".