Do rivers function as genetic barriers for the plateau wood frog at high elevations?
Bibliographic record
Abstract
Abstract Using the plateau wood frog Rana kukunoris from the Hengduan Mountains as a model system, we tested whether rivers form significant genetic barriers (the riverine barrier hypothesis) to high elevation amphibians. Samples were collected from eight sites across three major river drainages, the Min, the Dadu and the Yalong Rivers, and the population genetic structure of these frogs was evaluated with data from eight microsatellite DNA loci. A large amount of genetic structure was found, and the pairwise F ST ranged from 0.022 to 0.508 and a global F ST was 0.215. Both analysis of molecular variance and isolation by distance analysis suggested that rivers, mountain ridges and geographic distances all contributed significantly to the population structure. However, no single landscape has prominent barrier effect to the plateau wood frog populations. An assignment analysis using the computer program Structure grouped the eight populations into four population clusters, and no single type of landscape can sufficiently explain the clustering. In conclusion, rivers do not appear to be the leading genetic barriers for the plateau wood frog. The strong population genetic structure is likely the consequence of attributes of the species, as opposed to environmental fragmentation, and the barrier effect of the landscapes is largely swamped by the large amount of intrinsic population structure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".