Are riparian habitats always more diverse than nonriparian? A case study with small mammals in a rainforest environment
Bibliographic record
Abstract
Riparian environments are characterized by a gradient of environmental factors perpendicular to the watercourse, as the habitat changes from terrestrial to aquatic. These areas are highly diverse in comparison with adjacent ecosystems specially in arid and semi-arid regions, a pattern that may not be as marked in other climates where humidity and nutrient gradients are not so abrupt. We aimed to evaluate the diversity of small mammals in riparian and nonriparian environments in an area of Atlantic Forest, as well as the association between habitat structure and small mammal assemblages. A survey was conducted between October 2018 and August 2019 by sampling 17 plots—8 in riparian areas and 9 in nonriparian areas. No differences were found in composition and abundance of small mammals between riparian and nonriparian environments, because habitat structure did not differ between these environments. However, small mammal assemblages were structured by habitat characteristics such as understory obstruction, fallen trunks, and altitude. Water deficits are not marked throughout the year in the study area, therefore there is no such distinction between riparian and nonriparian environments. The most important habitat characteristics for the small mammals were those that represent shelter and resources’ sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".