Nutritional Evaluation of Millet Plants Grown in Soils Fertilized With Organic Wastes From Different Sources
Bibliographic record
Abstract
The losses of essential elements to crops make necessary to correct soil fertility to meet the nutritional requirements of plants, which can be achieved by increasing soil organic matter. The objective of this work was to evaluate the leaf nutritional conditions of millet plants grown in soils fertilized with organic wastes from different sources at different rates. Organic matter can make the soil more productive and suitable to agricultural crops. A randomized block experimental design with a 4 × 2 factorial arrangement was used with four replications, consisting of 32 experimental units. The treatments consisted of four organic matter sources (swine manure, sewage sludge, bovine manure, and poultry litter), and two organic matter rates (20% and 40% of the pot volume). Boron extraction was performed by dry digestion—the organic matter of the plant tissue was incinerated in an electric muffle furnace at 450-550 ºC, and the inorganic residue (ash) was dissolved in a dilute acid solution. N, K, Ca, Mg, S, Fe, Cu, Mn, and Zn was extracted through wet digestion—the organic matter of the leaf tissue was oxidized by concentrated mineral acids and by heat. N, P, S, B, Ca, Mg, Cu, Fe, Mn, and Zn was determined by spectrophotometry. K was determined by flame photometry. All macro and micronutrient contents in the millet leaves, and biometric parameters of the millet panicle were affected by the organic matter sources, organic matter rates, and the interaction between them.
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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 | 0.001 |
| 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".