Speciation in the <i>Rana chensinensis</i> species complex and its relationship to the uplift of the Qinghai–Tibetan Plateau
Bibliographic record
Abstract
Speciation remains a fundamental issue in biology. Herein, we report an investigation into speciation in the Rana chensinensis species complex using DNA sequence data from one mitochondrial and five nuclear genes. A phylogenetic analysis of the data revealed four major clades in the complex, and each of them was found to likely represent a species, including one cryptic species. Ecological niche models were generated from 19 climatic variables for three of the four major clades, which were represented by widespread sampling, including R. chensinensis, Rana kukunoris and the potential cryptic species. Each clade is associated with a unique ecological unit, and this indicates that ecological divergence probably drove speciation. Ecological divergence is likely related to the late Cenozoic orogenesis of the Qinghai-Tibetan Plateau. In addition, gene flow between species was detected but only in peripheral portions of the ranges of the four major clades, thus likely had little influence on the speciation processes. Discordances between mitochondrial and nuclear genes were also found; the nominal species, R. chensinensis, contains multiple maternal clades, suggesting potential mitochondrial introgression between R. chensinensis and R. kukunoris.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".