Natural Regeneration Under Influence of Sustainable Management Plan in Caatinga
Bibliographic record
Abstract
Despite being one of the most heterogeneous Brazilian biomes, regardless of being the least known, the Caatinga is under strong anthropism. In this way the forest management offers techniques that, when used, guarantees the maintenance of the sustainability of the productive system, sustainability that is guaranteed through the resilience of the regenerative stratum. Thus the objective of this work was to evaluate the effect of forest management on natural regeneration in a caatinga area, under different cutting ages. The study was developed in the settlement of the agrarian reform, Brandão III, located in the city of Cuité-PB. To evaluate the natural regeneration, 40 plots measuring 5 × 5 m (25 m2) were randomly allocated within the plots exploited and in the Legal Reserve. All individuals with Circumference at baseline (CNB) ≤ 6 cm and with a minimum height of 0.5m were measured, and distributed in three height classes. A C1: 0.5 m < H < 0.99 m; C2: 1.0 m ≤ H < 1.99 m and C3: H > 2.0 m. The data of density, richness and number of individuals by type of regeneration origin were compared by the Tukey test at 5% significance. We sampled 2021 individuals, represented by 32 species, 27 genera distributed in 16 families. The exploration did not cause significant changes to the floristic composition nor to the richness of the species. The exploration and the time elapsed between the cut and the measurement influenced the increase of the density.
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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".