Genetic diversity of mocó cotton (<i>Gossypium hirsutum</i>race<i>marie-galante</i>) from the northeast of Brazil: implications for conservation
Bibliographic record
Abstract
Mocó cotton ( Gossypium hirsutum L. race marie-galante (Watt) Hutch.) is a potential source of valuable alleles for breeding programs, mainly because of its great adaptability to semi-arid conditions. With the aim of quantifying mocó cotton genetic variability, 187 plants collected in the northeast of Brazil were evaluated using 12 microsatellite markers. A total of 62 alleles were amplified, ranging from three to eight polymorphic alleles per locus. Total genetic diversity was high (0.52), and when measured on a per state basis, was of 0.37 on average. The population showed a low level of heterozygozity (HO= 0.16), reflecting a high level of endogamy (FIS= 0.67). Phylogenetic analysis using the neighbor-joining method revealed that plants sampled in different states tended to cluster according to their geographic origin, except for those collected in the states of Paraíba and Rio Grande do Norte, which grouped together. Plants from the state of Piauí formed two groups, one with an apparent allelic contribution from G. barbadense, while the second group of plants was closer to those from the states of Paraíba and Rio Grande do Norte. Despite the high genetic diversity that was observed in the remaining populations, urgent conservation efforts should be undertaken, owing to the high level of endogamy and accelerated extinction process that characterizes these populations. Such efforts should focus on the collection and ex situ maintenance of representative genetic diversity.
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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".