Eucalyptus productive matrix influences the soil seed bank in southern Brazil
Bibliographic record
Abstract
Eucalyptus species are cultivated for various purposes around the world, mainly because they adapt well and grow quickly. These productive matrices of planted forests are common in Brazil, added to which, areas of native vegetation are requirements of environmental legislation. Although eucalyptus plantations are widely distributed in Brazil, little is known about the soil seed bank (SSB) in the eucalyptus productive matrix (EPM). We aim to understand how the EPM made up of eucalyptus plantations and native forest remnants affect the SSB in the Brazilian Pampa. Samples of the SSB of EPM were collected and monitored for 6 months in a shade house. We evaluated seedling emergence, richness, composition, ecological characteristics of the species, diversity, and floristic similarity. We recorded a high rate of seedling emergence, species richness, and different life forms and native species in all EPM treatments; however, the similarity between native remnants and eucalyptus plantations was low. The size and level of conservation of native remnants and the position and management of eucalyptus sites in the EPM influenced the diversity and composition of species. Eucalyptus plantations contain SSBs with potential for natural regeneration when they are in a landscape that maintains conserved native forest remnants. These results expand the knowledge of the SSB in EPM and can support actions in ecological restoration projects.
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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 | 0.000 |
| 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".