A Re-Assessment of Priority Amphibian Species of Peru
Why this work is in the frame
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Bibliographic record
Abstract
Peru supports approximately 588 amphibian species, of which 492 have been assessed on the IUCN Red List of Threatened Species. Of these, 111 are classified as Threatened, with 69 species classified as Critically Endangered or Endangered. In addition, 140 amphibian species remain Data Deficient. We re-assessed the conservation status of 38 amphibian species originally identified as potentially Threatened by von May et al. (2008), using the IUCN Red List Categories and Criteria. Fourteen species assessments changed as a result of re-assessment, of which eight changed from Data Deficient to Threatened; two changed from Data Deficient to Near Threatened and Least Concern respectively; two were up-listed from Least Concern to a Threatened status; two were down-listed. None of the changes were due to a known genuine change since the previous assessment. All changes were justified by an increase in knowledge. The eight species with a change from Data Deficient to a Threatened category belonged to four anuran families: Craugastoridae, Dendrobatidae, Hemiphractidae and Telmatobiidae. The reasons for a change in assessment status were: changes in taxonomy, distribution, population status, threat status, or previously incorrect information. The main threat affecting re-assessed amphibian species was habitat loss, with other threats including pollution, disease outbreaks, and collection for the pet trade. Only 53% of the re-assessed species were found to occur in a protected area. Findings of this study indicate the continuing fragility of many Peruvian amphibians and highlight the need for improving their protection and for further research into their population status and threats.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it