Composition and Diversity of Areas Under Restoration Process From the Brazilian Atlantic Forest
Bibliographic record
Abstract
The evaluation of the forest restoration scenario is of great importance, with floristic composition and diversity being among the most used ecological variables as indicators. This research aimed to identify the current situation, in terms of species composition and diversity, of two riparian forests under restoration based on a reference ecosystem, the Brazilian Atlantic Forest. Twenty permanent plots (250 m²) were located in areas under restoration process (AR1 and AR2) and in a forest fragment of the region which served as a reference ecosystem (ER). We sampled, identified and classified all tree individuals with CBH ≥ 15.0 cm in each plot. Aiming to understand species richness and diversity, besides the traditional indexes (Shannon and Simpson), we also estimated the effective numbers of Hill’s diversity (qD = 0, 1 and 2) considering rarefaction (P ≤ 0.05); and to detect floristic similarities among the study areas we performed a Principal Coordinate Analysis (PCoA). We found dissimilarity among ARs and ER, and the presence of exotic species, indicating that, as recommended, such reference should have been taken into account during the planning of the restoration action. Considering the effective numbers of species (qD) we found differences between the areas, species richness and diversity was higher in ER > AR2 > AR1. We also show that among the restoration areas, with the same age and submitted to the same conditions, AR2 has features that allow us to conclude that this area has a bigger chance of success in the restoration process. However, aiming environmental sustainability, we suggest that some corrective actions should be taken in order to favour the reestablishment of ecological processes in these areas.
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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 | 0.000 |
| 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".