Genetic Diversity in Mimosa tenuiflora (Willd.) Poir.: A Multipurpose Plant Genetic Resource of Semiarid Brazil
Bibliographic record
Abstract
Caatinga is the third largest biome in Brazil but little is known about the species diversity from this biotic community, despite of its social, economic and environmental importance for the semiarid region. Among the several typical plant species from Caatinga, Mimosa tenuiflora (Willd.) Poir. (black jurema) stands out because it plays a major role in the maintenance of this ecosystem, besides being widely used to recover degraded areas. Therefore, the goal of this study was to evaluate the genetic diversity and structural analysis from 10 populations of M. tenuiflora from the state of Bahia, northeastern Brazil, using 10 ISSR (Inter Simple Sequence Repeat) markers. A total of 117 fragments were obtained from 218 individuals with a mean number of 11.8 bands per primer. The mean population polymorphism was 85.0%, while the values of genetic diversity (He) and the Shannon index (I) were equal to 0.295 and 0.442, respectively. Most of genetic variation was observed (87.0%) but high FST values were observed (0.132), indicating the populations are genetically differentiated. Bayesian inference using Structure divided the populations into two groups while Geneland indicated five clusters that could be related to the fragmentation of Caatinga and to constraints in the dispersal of pollen and seeds. In conclusion, M. tenuiflora presents high levels of genetic diversity and natural populations might serve as potential sources for management and reforestation of degraded areas in Caatinga.
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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 | 0.001 |
| 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".