Characterization of the Genetic Resources of Farmed Tambaqui in Northern Brazil
Bibliographic record
Abstract
The present study analyzed the genetic variability and structure of farmed tambaqui in the Brazilian state of Pará, and provided basic information that can be used for the development of programs of monitoring and management of genetic resources in the aquaculture operations of northern Brazil. A total of 216 individuals were sampled from tambaqui farms in Pará. Genotyping was based on a multiplex set of 10 tri- and tetra-nucleotide microsatellite markers. The data were used to calculate genetic diversity indices, expected and observed heterozygosity, the number of alleles per locus, allelic richness, and inbreeding coefficient. Genetic structure was verified using DEST and RST, the genetic signature, and Bayesian analysis. The results showed that the tambaqui farms surveyed have suffered a significant loss of genetic variability, and that they are genetically structured, forming two clusters, one encompassing the farms in western Pará, and the other including the farms from the northeast and southeast regions of the state. These finding provide fundamental insights for the development of effective strategies that will help guarantee productivity and the quality of the tambaqui farms of northern Brazil, and provide a database for the upgrading of the genetic variability of these populations. This study indicated the need for hatcheries in southeastern and northeastern Pará to amplify or renew their breeding stocks, in order to avoid the significant loss of genetic diversity in the tambaqui farms of these regions.
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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 | 0.001 | 0.001 |
| 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 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".