Amphibians on the hotspot: Molecular biology and conservation in the South American Atlantic Rainforest
Bibliographic record
Abstract
Amphibians are the focus of a recent debate and public attention owing to the global decline in their populations worldwide. Amphibians are one of the most threatened and poorly known groups of vertebrates in several geographic areas, even though they play a central role in their own ecosystems. At different levels, amphibians make their contribution to proper ecosystem functioning. They act as regulators of the food web and nutrient cycling, and they also provide several valuable ecosystem services, e.g., as a food source and as animal models for lab research. In this sense, it seems clear that the maintenance of amphibian diversity should be one of the major goals for the several countries where their population decline is observed. However, we are still struggling with the very first step of this process, i.e., the correct identification of the amphibian species diversity. Over the past few decades, research on molecular identification of amphibians using DNA barcoding has encountered some difficulties related to high variability in the mitochondrial genome of amphibians, and a research gap is noticeable in the literature. We herein evaluated both COI and 16S rRNA mitochondrial genes for the molecular identification of frogs and tadpoles in a large fragment of the South American Atlantic Rainforest in Rio de Janeiro, Brazil. Our results suggest that both COI and 16S rRNA are informative markers for the molecular identification of the amphibian specimens with all specimens unambiguously identified at the species level. We also made publicly available 12 new sequences of Atlantic Rainforest amphibian species for the first time, and we discussed some conservation issues related to amphibians within the Atlantic Rainforest domains in the state of Rio de Janeiro, Brazil.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".