Landscape components associated to forestry in the Atlantic rainforest influence the aquatic macroinvertebrate community: a case study in southern Brazil
Bibliographic record
Abstract
Several studies indicate that negative impacts on water quality are minimally related to forestry. We analyzed the water quality of a stream in a silvicultural region in southern Brazil, considering the relationship between the components of the landscape and biotic quality indexes, merging physical and biological descriptions of the macroinvertebrate community and environment. We selected three points in Faxinalzinho stream to collect macroinvertebrate samples and to perform perceptual analysis from September to December/2014, applying the Biological Monitoring Working Party (BMWP') and the Rapid Assessment Protocol for Habitat Diversity (RAPHD). Diversity metrics and a Non-metric Multidimensional Scaling (NMDS) were also applied to explore variations in the aquatic community abundance matrix, associating the data to Land Use/Land Cover. The results showed good water quality in the studied points, mainly when compared to urban rivers. However, we found negative effects in the site with higher forestry land cover, presenting acceptable water quality and altered environmental condition according to BMWP’ and RAPHD, while the other sites presented excellent water quality and natural environment, respectively.
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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 | 0.001 | 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".