Diameter Distribution of Vouacapoua americana Aublet in the Brazilian Amazon
Bibliographic record
Abstract
Vouacapoua americana Aublet was classified as endangered in 2013 and its extraction banned in 2014. Forest management allows conservation and sustainable production, but, for this, knowledge of diameter distribution is fundamental. This study aimed to characterize and analyze diameter distribution patterns of the species at different sites in the Brazilian Amazon. Data on trees with diameter ≥ 10 cm were acquired from continuous forest in permanent sample plots and preharvest forest inventories (PHFIs) of nine forest management areas. Absolute density, diameter distribution, and De Liocourt quotient (q) were calculated. Diameter distributions were fitted by a linearized Meyer type I distribution function, and the similarity between distributions was analyzed by the nonparametric Kruskal-Wallis test (H-test). The species showed high density (6.31 to 25.55 trees/ha). Mensrured diameters ranged from 10.00 to 127.32 cm. A decreasing behavior was observed in all diameter distributions, with few discontinuous distributions and mostly truncated distributions. The De Liocourt quotient (q) did not show constancy or proximity, with values ranging from 0.4 to 23.48. Diameter distributions did not differ by the Kruskal-Wallis test (H = 15.45, p = 0.3479). Diameter distributions fitted by the Meyer model resulted in an inverted “J”-like curve. The diameter structure showed a high density of individuals, a decreasing distribution from smaller to larger diameter classes, a characteristic inverted “J” pattern, and unbalanced diameter distributions.
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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 | 0.000 | 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".