Agronomic Performance of Millet Plants Grown in Soil Fertilized With Organic Wastes
Bibliographic record
Abstract
Information on the potential of organic matter for soil fertility is important to an efficiently replenishment of nutrients. In this context, the objective of this work was to evaluate the agronomic performance of millet plants grown in soil fertilized with different rates of organic wastes, through biometric variables. 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 (10 and 20 dm3); 50 dm3 pots filled with an agricultural soil that is predominant in the region were used. The pots were filled with 80% of soil and 20% of organic matter (10 dm3); and with 60% of soil and 40% of organic matter (20 dm3). Plant height (PH), leaf area (LA), stem diameter (SD), and number of expanded leaves (NL) were evaluated at 20, 40, 60, and 80 days after sowing (DAS); panicle length (PNL), and panicle diameter (PD) were measured at 80 DAS. All biometric variables evaluated indicated that the better organic matter rate for soil fertilization for millet crops is 10 dm3. In general, the highest panicle lengths and diameters were found in plants grown in soil with bovine manure.
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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".