Abundance of tegu lizards (Tupinambis merianae) in a remnant of the Brazilian Atlantic forest
Bibliographic record
Abstract
Abstract We investigated the abundance of the tegu lizard (Tupinambis merianae) in a 21 787 ha Brazilian Atlantic forest fragment (Reserva Natural Vale, RNV). This remnant has a highly irregular perimeter and an extensive network (126 km) of internal unpaved roads. We hypothesized that the high proportion of these edge habitats might benefit active heliothermic lizards like tegus due to greater incidence of sunlight. We estimated population density using the program DISTANCE, and compared sighting frequency of tegus along twelve 500 m long transects located at three distances (25, 200 and 400 m) from the nearest unpaved road or fragment edge. We found no significant differences in sighting frequency among the three distances (Chi-square; χ2 = 4.308; P = 0.116) and no significant association between edge distance and edge type (internal, external) (G test adjusted; G2 = 0.617; P = 0.734). However, as the test comparing distances had relatively low power we assumed that the experimental evidence was not strong enough to prove lack of an edge effect. The estimated density (0.63±0.13 lizard/ha) is within the range of densities found on Brazilian islands where tegus have proliferated to the point of becoming a threat to ground nesting birds and turtles. We caution, however, that the absence of published data on other non-island sites prevents us from concluding that the species has an abnormally high density in RNV.
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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 | 0.000 |
| 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".