Conservation Genetics and Genomics of Amphibians and Reptiles
Bibliographic record
Abstract
Amphibians and reptiles as a group are often secretive, reach their greatest diversity often in remote tropical regions, and contain some of the most endangered groups of organisms on earth. Particularly in the past decade, genetics and genomics have been instrumental in the conservation biology of these cryptic vertebrates, enabling work ranging from the identification of populations subject to trade and exploitation, to the identification of cryptic lineages harboring critical genetic variation, to the analysis of genes controlling key life history traits. In this review, we highlight some of the most important ways that genetic analyses have brought new insights to the conservation of amphibians and reptiles. Although genomics has only recently emerged as part of this conservation tool kit, several large-scale data sources, including full genomes, expressed sequence tags, and transcriptomes, are providing new opportunities to identify key genes, quantify landscape effects, and manage captive breeding stocks of at-risk species.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".