Growth Rates and Sunflower Production in Function of Fertilization with Biochar and NPK
Bibliographic record
Abstract
The use of alternative sources such as biochar can contribute to sustainable agriculture. The objective of this study was to evaluate the effect of fertilization with biochar in combination with mineral fertilizer, growth rates and yield of sunflower cv. Embrapa 122/V2000. The experiment was carried out in a protected environment of UAEAg/CTRN/UFCG in pots with a capacity of 20 dm3 under a completely randomized experimental design, with treatments arranged in a factorial scheme (5 × 4) and four replicates corresponding to five doses of mineral fertilization 0, 25%, 50%, 75%, and 100% of fertilization indication with NPK (100, 300, and 150 mg kg-1) and four doses of biochar, 0; 5% (400 g/pot); 10% (800 g/pot) and 15% (1100 g/pot) calculated on the basis of the soil volume. Fertilization with biochar promoted an increase in sunflower growth rates in the evaluation periods. The combination of 50: 150: 75 mg kg-1 of NPK and 400 g/pot of biochar promotes higher production of sunflower (23.91 g/plant).
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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) | 0.001 | 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".