The Herpetofauna from Ilha Grande (Angra dos Reis, Rio de Janeiro, Brazil): updating species composition, richness, distribution and endemisms
Bibliographic record
Abstract
Ilha Grande is a large continental island (total area of 19,300 ha) situated at the southern coast of the state of Rio de Janeiro, in southeast Brazil, within the Atlantic Forest Biome. Here we provide an update to the previous knowledge of the fauna of amphibians and reptiles occurring in Ilha Grande, based on primary data from our own fieldwork and on secondary data (from institutional collections and from the literature). We report the occurrence at Ilha Grande of a total of 74 species, being 34 amphibians (all of them anurans) and 40 reptiles (27 snakes, 11 lizards, one amphisbaenian and one crocodylian). Our survey added 14 species to the herpetofaunal list of Ilha Grande (three of amphibians and eleven of reptiles) and removed one species (the amphibian Cycloramphus fuliginosus) from the previous list. The data indicated that Ilha Grande houses a considerable portion of the Atlantic Forest amphibian and reptile diversity (ca. 6% and 19%, respectively, of the species occurring in this biome) together with high occurrence of species endemic to this biome plus a few amphibian species endemic to this island. Ilha Grande is thus an important reservoir of both biodiversity and endemism of amphibians and reptiles of the Atlantic Forest of Brazil, which highlights the importance of the conservation of the island and of its different habitats along the insular landscape.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".