Biometric and Chemical Characterization of Fruits From Selections of Platonia insignis Mart., Native of the State of Maranhão, Brazil
Bibliographic record
Abstract
Brazil has a range of fruit species, especially native ones, which play an important role in the life of local populations, but are still little studied, as is the case of bacuri (Platonia insignis Mart.). P. insignis is a fruit tree species native from Amazon region and has great economic potential, mainly due to its excellent organoleptic and nutritional characteristics. Therefore, there is a need for research that seeks the proper use of the species, as well as the selection of superior genotypes. Thus, the objective of this study was to characterize biometric and chemically fruits of eight selections of native P. insignis plants, from the municipalities of Presidente Juscelino and Santa Rita both located in Lower Munim region, state of Maranhão, Brazil. For the biometric characterization, 20 fruits, individually, were analyzed as to mass, longitudinal diameter, transverse diameter, conformation index, seed number, parthenocarpic segments number, pulp yield and bark mass, and for the chemical characterization, samples composed of six fruits were analyzed as to soluble solids (°Brix), total acidity (% citric acid), soluble solids/acidity ratio and pH. There was significant difference for all characteristics evaluated. High coefficients of variation were observed, indicating variability among the selections and possibility of taking advantage for the genetic improvement. The results allow to indicate the fruits analyzed, both for the industrial market and for the in natura consumption, especially for the ‘Domingão’ and ‘Mamão’ selections.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".