Estimating population structure under nonequilibrium conditions in a conservation context: continent‐wide population genetics of the giant Amazon river turtle,<i>Podocnemis expansa</i>(Chelonia; Podocnemididae)
Bibliographic record
Abstract
Giant Amazon river turtles, Podocnemis expansa, are indigenous to the Amazon, Orinoco, and Essequibo River basins, and are distributed across nearly the entire width of the South American continent. Although once common, their large size, high fecundity, and gregarious nesting, made P. expansa especially vulnerable to over-harvesting for eggs and meat. Populations have been severely reduced or extirpated in many areas throughout its range, and the species is now regulated under Appendix II of the Convention on International Trade in Endangered Species. Here, we analyse data from mitochondrial DNA sequence and multiple nuclear microsatellite markers with an array of complementary analytical methods. Results show that concordance from multiple data sets and analyses can provide a strong signal of population genetic structure that can be used to guide management. The general lack of phylogeographic structure but large differences in allele and haplotype frequencies among river basins is consistent with fragmented populations and female natal-river homing. Overall, the DNA data show that P. expansa populations lack a long history of genetic differentiation, but that each major tributary currently forms a semi-isolated reproductive population and should be managed accordingly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".