Changes in decomposition rate and litterfall in riparian zones with different basal area of exotic<i>Eucalyptus</i>in south-eastern Brazil
Bibliographic record
Abstract
Exotic species in riparian environments can influence the quantity and quality of litterfall in the ecological system. The objective of this study was to evaluate the influence of Eucalyptus leaves on litterfall and terrestrial and aquatic leaf decomposition in a riparian forest in São Paulo state, Brazil. Three riparian zones were evaluated, and they were designated as areas of low (L), medium (M) and high (H) Eucalyptus basal area, respectively. Each riparian area was evaluated by surveying its structure and floristic composition. The amount of litterfall was evaluated over one year, and leaves were collected in terrestrial and vertical collectors. We evaluated the rates of aquatic and terrestrial decomposition of Eucalyptus and native leaves within litterbags. In general, area H had lower species richness, a higher edge effect and high Eucalyptus litterfall. Area L had higher species diversity (H′, Shannon– Wiener index), higher Pielou’s equitability index (J′), and smaller amounts of Eucalyptus litterfall. Eucalyptus leaves had higher extractive and lignin content compared with that of native trees. Eucalyptus leaves had a lower decomposition rate, except for the aquatic environment in area M. Our results show that the presence of Eucalyptus in riparian zones can increase litterfall and reduce the rate of leaf decomposition, but more studies are needed to evaluate any changes in ecosystem function from Eucalyptus presence.
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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 | 0.000 | 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".