River capture or ancestral polymorphism: an empirical genetic test in a freshwater fish using approximate Bayesian computation
Bibliographic record
Abstract
Abstract A headwater or river capture is a phenomenon commonly invoked to explain the absence of reciprocal monophyly of genetic lineages among isolated hydrographic basins in freshwater fish. Under the assumption of river capture, a secondary contact between populations previously isolated in different basins explains the observed genetic pattern. However, the absence of reciprocal monophyly could also arise under population isolation through the retention of ancestral of polymorphisms. Here, we applied an approximate Bayesian computation (ABC) framework for estimating the relative probability of scenarios with and without secondary contact. We used Cnesterodon decemmaculatus as a study model because of the multiple possible cases of river capture and the demographic parameters estimated in a previous mitochondrial DNA study that are useful for simulating scenarios to test both hypotheses using the ABC framework. Our results showed that, in general, mitochondrial DNA is useful for distinguishing between these alternative demographic scenarios with reasonable confidence, but in extreme cases (e.g. recent divergence or large population size) there is no power to discriminate between scenarios. Testing hypotheses of drainage rearrangement under a statistically rigorous framework is fundamental for understanding the evolution of freshwater fish fauna as a complement to, or in the absence of, geological evidence.
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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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".