Nutritional Status of Mango by the Boundary Line and Mathematical Chance Methods
Bibliographic record
Abstract
The objective of this study was to establish the optimal content and sufficiency range of the nutrients in mango of the cultivars Tommy Atkins, Kent and Keitt in the Sub-middle San Francisco Valley by the Boundary Line and Mathematical Chance methods and compare them to other nutritional diagnosis methods used in Brazil and Australia. The study was carried out in seven commercial farms cultivated with mangoes, located in the Sub-Middle São Francisco Valley. The database used was formed from the results of the analysis of leaves and the productivity of irrigated mango trees. For Boundary Line and Mathematical Chance methods were estimated the optimal nutrient content, as well as the sufficiency range and later were compared between them and with the optimal contents and sufficiency ranges recommended in literature. The optimal nutrient contents for mango tree cultivars by the Boundary Line and Mathematical Chance methods were close to the mean contents of the high productivity population, and also disagreed of the diagnoses of the methods the literature, suggesting that these methods were more consistent and expressed better the regional management of mango tree cultivation in the Sub-Middle São Francisco Valley. The Mo sufficiency range developed by the Boundary Line and Mathematical Chance methods made it possible to assess the nutritional status of this nutrient in the region. These results show the importance of creating and validating specific nutritional standards for the edapho-climatic conditions of the Valley, taking into account the production management and nutritional requirement of the cultivars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".