Isolation and characterization of 24 polymorphic microsatellite loci for the study of genetic population structure of the sheepshead Archosargus probatocephalus (Actinopterygii, Perciformes, Sparidae)
Bibliographic record
Abstract
BACKGROUND: The sheepshead (Archosargus probatocephalus) is found in nearshore waters from Nova Scotia, Canada, to Rio Grande do Sul, Brazil. In the southeastern United States two subspecies are recognized based on a number of meristic characters, primarily counts of melanistic pigment bars. The only previous study based on mtDNA control-region sequence found limited divergence between those subspecies and isolation by distance among 15 locations from Florida (Atlantic Ocean) to Texas (Gulf of Mexico). In the same study, using six sparid microsatellite markers, Bayesian analysis showed that the Gulf and Atlantic sheepshead form a single population. To reinvestigate the fine-scale genetic population structure and examine genetic support for the morphologically classified subspecies, a set of species-specific microsatellite markers was needed. FINDINGS: Here we report on 24 polymorphic microsatellite markers isolated from sheepshead and screened in 57 specimens from the Indian River, Florida. The average number of alleles per locus was 13.1; mean observed and expected heterozygosities were 0.68 and 0.73, respectively. Nine sparid markers screened for the same specimens showed an average of 8.6 alleles per locus; mean observed and expected heterozygosities were 0.46 and 0.55, respectively. CONCLUSIONS: The polymorphic markers reported here can be used to search for genetic evidence for the morphologically defined subspecies, to elucidate the fine-scale genetic population structure of this broadly distributed coastal species, and to provide an opportunity to directly compare results of population delineation between nonspecific and species-specific markers.
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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".