Mountains too high and valleys too deep drive population structuring and demographics in a Qinghai–Tibetan Plateau frog <i>Nanorana pleskei</i> (Dicroglossidae)
Bibliographic record
Abstract
Abstract Pleistocene glacial–interglacial climatic oscillations greatly shaped the current genetic structure of many species. However, geographic features may influence the impact of climatic cycling. Distinct geographic and environmental characters between northern and southern parts of the eastern Qinghai–Tibetan Plateau ( EQTP ) facilitate explorations into the impacts of geographic features on species. The northern parts of EQTP contain large areas of marsh, and the environment is rather homogeneous. In contrast, the southern EQTP harbors complex alpine valleys and a much more heterogeneous setting. We evaluate DNA sequence variation from both the mitochondrial and nuclear genomes in Nanorana pleskei , a species endemic to the EQTP . Hypothesis testing on the evolutionary history of N. pleskei indicates that northern populations can disperse freely, but alpine valleys isolate southern populations. Demographic histories between northern and southern populations also differ. Northern populations appear to have experienced population expansions, while southern frogs exhibit a far more stable demographic history. By combining climatic analyses and species' distribution models, our study suggests that geographic and environmental features drive the differences between the northern and southern EQTP .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".