Biochar, Filter-Cake, and Polymer-Based Special Fertilizers for Maize Cultivation
Bibliographic record
Abstract
This investigation assessed the effects of special fertilizers on maize. The experiment was performed according to a randomized block design, with four replications in a 7 × 4 + 1 factorial scheme, representing seven sources (filter-cake-based organic compound, biochar-based organic compound, organic-mineral (OM) filter-cake, OM biochar, OM filter cake + humic substances (HS), mineral fertilizer with polymers, and conventional mineral) and four fertilizer levels based on the nitrogen (N) contents (60, 120, 180, and 240 kg/ha). An absolute control, without any fertilizer application, was used. At 60 days after sowing (DAS), the plant growth parameters, nutritional status, and residual nutrients in the soil were evaluated. The mineral fertilizer with polymers and OM filter-cake + HS showed better results for the dry matter of the shoot of the plant and plant height. When considering the leaf area, the OM filter-cake and OM filter-cake + HS fertilizers stood out. The accumulation of N and potassium (K) in the shoot of the plants was higher when the OM filter-cake + HS and mineral fertilizer with polymers were applied. With respect to the accumulated phosphorus (P), treatment with the OM filter-cake, OM filter-cake + HS, and mineral with polymers showed better results. On the other hand, for the analysis of P and residual K in the soil, the treatments based on filter-cake and biochar organic compost were better. Treatment with the OM filter-cake + HS and mineral with polymers stood out when considering the plant growth parameters and nutritional aspects of the maize crop.
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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) | 0.001 | 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) | 0.000 | 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".