Nutrient Omission Effects on Growth and Nutritional Status of Seedlings of Assai Palm (Euterpe oleracea Mart.) var. Pai d’égua in Clayey Oxisol
Bibliographic record
Abstract
In order to evaluate the growth, dry mass production, contents and accumulation of macro-and micronutrients in seedlings of assai palm (variety Pai d’égua) in clayey Oxisol we conducted a greenhouse experiment based on the missing element technique. The experimental design was completely randomized with 15 treatments in five replicates. The treatments were: complete fertilizer with liming (complete); no fertilizer and no liming (control); complete fertilization with lime but with the individual omission of nitrogen, phosphorus, potassium, calcium, calcium without lime, magnesium, magnesium without lime, sulfur, boron, copper, manganese and zinc. The following variables were analyzed: plant height, stipe diameter, leaf dry mass, stipe dry mass, and content and accumulation of nutrients in the leaves. The singly omission of N, P and Mg has limited the height of the assai palm. The following singly omissions in decreasing order: N > K > Mg affected the production of leaf dry mass, while the leaf area was restricted by the individual omissions, in order: N > P > K. Plant development as measured by relative growth of the aerial part is affected by the lack of P > K > N > Mg with an average reduction of 61.9%. The nutrients most required by assai palm follow the order: N > K > Ca > Mg > P > Mn > Zn > B > Cu > S.
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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".