Assessing the nutritional status of Southern Brazilian <i>Eucalyptus</i> plantations by the CND method
Bibliographic record
Abstract
Genus Eucalyptus can be grown in different soils worldwide, although it is not always ready to fulfill plants’ nutrient demands. Whenever such nutrient shortage happens, it is necessary applying the nutrients missing, which can be established based on the critical levels and sufficiency ranges (SRs) of nutrients in leaves or by multivariate mathematical models, such as the composition nutrient diagnosis (CND). This study aimed to investigate the nutritional status of Eucalyptus plantations in Brazil, based on the CND method. A total of 119 12-month-old Eucalyptus saligna (E. saligna) plantations were sampled in the Rio Grande do Sul State (RS), Southern Brazil. Nutrient concentration in leaves and the diameter at breast height (DBH) was measured. The E. saligna nutritional status was calculated through the CND method. High- and low-yield populations were set based on DBH of 4.2 cm. The SRs proposed by the CND method were narrower than the ones proposed by official recommendations for Eucalyptus, especially for magnesium (Mg), boron (B), and iron (Fe). The CND-r2 index recorded for each nutrient generated a limitation order for nutrients. The greater accuracy of the recommendations proposed by the CND method, compared with univariate and bivariate methods, contribute to reducing the use of fertilizers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".