Macronutrient Omission Affects the Seedling Performance of Eugenia dysenterica DC., an Important Fruiting Species of the Cerrado Biome
Bibliographic record
Abstract
Cagaita (Eugenia dysenterica DC.) seedling production is currently important for the restoration of degraded areas of the Cerrado biome and to supply the demand for its fruits for regional food production. The plants of Cagaita have been described in the literature as resistant to nutrient-poor soils, but the studies remain inconclusive. Therefore, we tested the hypothesis that initial E. dysenterica seedling development could be affected by the lack of specific nutrients. We planted seedlings of this species in complete hydroponic solution and under individual macronutrient omission of N, P, K, Ca, Mg and S. The development of macronutrient deficiency symptoms was visually monitored for 120 days after transplantation in response to the treatment solution, and growth characteristics and tissue contents of different macronutrients were determined. The omissions of Mg and P were the treatments with the strongest effects on seedling growth. The most commonly observed deficiency symptoms were chlorosis and necrosis, in addition to leaf purpling (P deprivation), impaired root hair development (Ca and S deprivation) and a reduced number of leaves (Mg and P deprivation). The macronutrients Ca and N were most accumulated in the shoots and roots of plants subjected to complete nutrient solution, showing the importance of the availability of these nutrients in the initial stages of seedling development for the production of healthy Cagaita plants, with greater viability in the field.
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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".