Camu-Camu: Nutrient Omission Response and Soil Acidity Correction
Bibliographic record
Abstract
Camu-camu is an Amazonian fruit that has high levels of vitamin C, however, there is a need to expand knowledge to carry out systematic and consistent studies in the various fields of knowledge, and those related to mineral nutrition. The objective of this work was to evaluate the nutritional and growth status of camu-camu by the missing element technique and the use of liming, using as substratum a dystrophic Yellow Latosol of central Amazonian texture. The experimental design was a randomized block design with four replications and 15 treatments: complete, liming omission, individual omission of N, P, K, Ca, Mg, S, Zn, Mn, Cu, B, Cl and Mo. of the witness (natural soil). The characteristics evaluated were: height, neck diameter, leaf, stem, shoot, root and total dry matter, relative growth, shoot and root ratio, and nutrient accumulation of shoot (leaf) dry matter. All data evaluated were statistically significant. Liming and fertilization were necessary for acidic and low natural soils when comparing the complete with the control. Ca and P were the most limiting nutrients, while omission of N reduced the growth of seedlings. Based on total dry matter, the nutritional requirement of camu-camu was in decreasing order: Ca > P > S > Cl > Cu > Mg > Z > K > Mo > Mn > B > N.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".