Geological events play a larger role than <scp>P</scp>leistocene climatic fluctuations in driving the genetic structure of <i><scp>Q</scp>uasipaa boulengeri</i> (<scp>A</scp>nura: <scp>D</scp>icroglossidae)
Bibliographic record
Abstract
Paleoclimatic and paleogeological events have been identified as being the two main drivers of genetic structuring in extant organisms. We used a montane stream-dwelling frog, Quasipaa boulengeri, to explore the relative roles played by these drivers on species in southern China, a region needing thorough studies. We detected four major matrilines, and no broadly distributed haplotypes occurred. The complex orogenesis of south-western China drove matrilineal divergence in Q. boulengeri into highly structured geographical units. These matrilines subsequently persisted in situ with stable populations rather than undergoing expansions during glacial cycling. The unification of the upper and middle Yangtze River in the Three Gorges mountain region mediated downstream colonization of this frog. Analyses identified geological events as playing a larger role than climatic fluctuations in driving the population history of Q. boulengeri. Nuclear allele analyses indicated gene flow; this maintained genetic cohesion of the species. South-eastern Sichuan Basin was identified as the area of secondary contact for several matrilines, and this area deserves further study and special protection.
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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) | 0.002 | 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".