Snakes from the Atlantic Rainforest area of Serra do Mendanha, in Rio de Janeiro state, southeastern Brazil: a first approximation to the taxocenosis composition
Bibliographic record
Abstract
We studied the species composition of the snake community of Serra do Mendanha, in Rio de Janeiro state, Southeastern Brazil, with an effort of 800 hours/man in different habitats, including undisturbed forest, secondary forest, areas under regeneration, and banana plantation. We sampled snakes monthly in the area using a combination of methods including intensive visual searching and pitfall traps with drift-fences. We found a total of 191 individuals of 27 snake species, belonging to four families: Boidae, Colubridae, Elapidae and Viperidae. In terms of species richness, the most speciose snake family in the area was Colubridae (85.2%; n = 23), followed by Viperidae (7.4%; n = 2), Boidae (3.7%; n = 1) and Elapidae (3.7%; n = 1) (Table 1). Quantitatively, the family Colubridae represented 81.7% (n = 156) of the total of individuals captured throughout the study, followed by Elapidae (13.1% of the individuals; n = 25), Viperidae (4.7%; n = 9) and Boidae (0.5%; n = 1). The data obtained in the study allowed a first approximation of the richness and composition of the snake fauna from Serra do Mendanha, including the records obtained during fieldwork in the present study and those of specimens deposited in Institutional Collections and detailed field data for each voucher specimen. All records are novel data for the area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".