Functional traits as an indicator of urbanization impact on the scorpion assemblage in Neotropical forest
Bibliographic record
Abstract
Currently, natural forests face significant loss of vegetation cover linked to habitat loss due to urban expansion. Therefore, we investigated the effects of changes in different land uses and land cover at the landscape scale resulting from the urbanization process on the functional traits of the body of scorpions in the Brazilian Atlantic Forest. In 10 forest fragments in Paulista, Pernambuco, we observed that forest cover had a statistically significant negative relationship with the average functional body traits of the scorpion assemblage. Distinct functional body traits were associated with the conditions of the surrounding landscape. Forest units with lower forest cover harbored species with higher average functional body traits, indicating adaptation to the urban scenario. Conversely, in areas with greater forest cover, species with smaller functional body traits predominated, suggesting habitat sensitivity and dependence on less disturbed environments. Our results indicate that scorpions may be indicators of changes in functional body traits due to land use change. These findings highlight significant implications for biodiversity conservation in the Atlantic Forest in the face of urban expansion, emphasizing the role of scorpions as key indicators of changes in functional body traits in response to the altered 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 | 0.000 |
| 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".