Genetic Diversity of Remaining Populations of Mangaba (Hancornia speciosa Gomes) in Restingas of Brazil
Bibliographic record
Abstract
Mangaba (Hancornia speciosa Gomes) is a fruit species that is native to Brazil, and has social, economic and cultural importance. Knowledge of the genetic relationships between the remaining populations is essential in order to promote conservation strategies for these genetic resources. In the present study, it was evaluated the genetic diversity of 35 individuals from three remaining restingas areas in the states of Ceará (Iguape and Cascavel) and Pernambuco (Tamandaré), located in the Brazilian Northeast. Nine ISSR primers were used to determine the genetic variability. Sixty-one fully polymorphic fragments (100%) were generated. The largest (10) and smallest (5) number of fragments were obtained with the primers HB14 and HB12, respectively. The Shannon index (I = 0.40), the genetic diversity (H = 0.30), and the percentage of polymorphic loci (%P = 73.77%) were also estimated. Both the methods of UPGMA and the Principal Coordinates Analysis (PCoA) clustered individuals according to their place of origin. Genetic divergence was greater within population (64%) than between them (36%). This may indicate a strong genetic structure, i.e., the gene flow rate between populations is low, favoring inbreeding. ISSR markers were efficient for the analysis of genetic diversity, for the identification of clusters, and for the estimation of the genetic distance between and within populations.
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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 | 0.001 | 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 | 0.000 |
| 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 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".