Evaluating the demographic significance of genetic homogeneity using a coalescent-based simulation: a case study with gag (Mycteroperca microlepis)
Bibliographic record
Abstract
Gag ( Mycteroperca microlepis ) from the eastern Gulf of Mexico and northwestern Atlantic are managed as separate stocks, although evidence for their demographic isolation remains equivocal. Several hundred individuals were genotyped at 11 microsatellite loci and it was found that gag across these regions were genetically indistinguishable (FST < 0.001). A coalescent-based computer simulation was employed to quantitatively assess the relative importance of dispersal rate, population size, and time since divergence on gag genetic homogeneity. Using empirical estimates of long-term effective population size (16 500), a range of dispersal rates and divergence times (500 to 500 000 generations) was modeled, and it was concluded that present-day genetic homogeneity must be maintained by dispersal rates of at least 20–30 migrants per generation and up to hundreds or thousands per generation. This study also documents the absence of significant temporal genetic structure and inbreeding in the Atlantic when comparing cohorts separated in time from weeks to 20 years. This suggests that the long-standing practice of overfishing gag has yet to manifest as an inbreeding effect. Overall, gag dispersal rates remain uncertain, and consequently, this study can neither support nor refute management schemes that independently regulate gag in the eastern Gulf of Mexico and northwestern Atlantic.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".