Influence of the environmental heterogeneity of breeding ponds on anuran assemblages from southeastern Brazil
Bibliographic record
Abstract
We hypothesized that the environmental heterogeneity of breeding ponds influences the species composition and species richness of anuran assemblages from southeastern Brazil, because it provides humidity, shelter, and breeding microhabitats for anuran species, which can result in an increasing number of species in a given habitat. To begin, we tested whether the occurrence of anuran species in each breeding pond is different from a null model of random placement of species in those ponds. We then performed two tests to evaluate which of the five environmental descriptors of breeding ponds influence (1) the species composition and (2) species richness. Species composition of the 38 breeding ponds was correlated with number of edge types, number of plant types along the edges of the breeding ponds, and the hydroperiod. Neither the percentage of vegetation cover on the water’s surface nor the size of the breeding ponds were correlated with species composition. Only the number of edge types was correlated with species richness of breeding ponds. The correlation of three environmental descriptors with species composition and one environmental descriptor with species richness, as well as the high beta diversity among breeding ponds, suggest that the analyses of environmental heterogeneity on species composition was more informative than was the analysis for species richness, because breeding ponds with similar species richness can have distinct species composition among them (high beta diversity).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".