ANALYSIS OF THE EROSION POTENTIAL AND SEDIMENT YIELD USING THE INTERO MODEL IN AN EXPERIMENTAL WATERSHED DOMINATED BY KARST IN BRAZIL
Bibliographic record
Abstract
Effective management of nutrient application is important part of the crop production puzzle and it seems that nano-fertilizers may have high potential for achieving sustainable crop production. A field experiment was carried out to investigate the effect of adding different nano-size and biological fertilizers on maize growth under various irrigation regimes. The experiment conducted under optimal irrigation level (up to ~50% field capacity) which is applied from the beginning of the reproductive period. Fertilizer's treatments included control (Nf; no-fertilizer application), N biofertilizer (Bio-N), P biofertilizer (Bio-P), nanochelated B (Nano-B), nano-chelated Zn (Nano-Zn), complete nano-fertilizer (Nano-C) and conventional mineral NPK fertilizer. Bio-P was the best treatment in terms of grain yield, ear length, biological yield, number of the kernels per row, length of ear leaf and straw yield traits, while Nano-Zn was the best treatment for increase of protein content and Nf was the best treatment for increase of oil content. Bio-N was the best treatment in terms of leaf area, ear diameter and hundred grain weight, while Nano-B was the best treatment for plant height, harvest index, stem diameter, number of the row per ear and number of the kernels per ears traits. Nano-C and NPK are not outstanding for any of the traits. Nano-Zn had good effect on high yield and high protein content while nano-B was good for better performance of plant height, stem diameter, number of the row per ear, harvest index and number of the kernels per ears traits. Such an outcome could be used in the future to advise good recommendation strategies for recommendations for maize and other crops in other areas of the world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".