Changes in forest structure and composition in a successional tropical dry forest
Bibliographic record
Abstract
We describe changes in forest structure and floristic composition of three successional stages for Mata Seca State Park, in Minas Gerais, Brazil, through the measurement of all trees greater than 5 cm of diameter at breast height (DBH) of 18 permanent plots (6 per stage) for early, intermediate, and late successional stages of a tropical dry forest during a 5-year period. Using this information, we calculated the Importance Value Index (IVI), Holdridge Complexity Index, Jaccard Similarity Coefficient, and Shannon Diversity Index for each stage of succession. The floristic composition and structure of the successional stages expressed by the Holdridge Complexity Index, showed that complexity increases gradually as we advance through the successional stages, while the Shannon Diversity Index indicated that species diversity was higher in the intermediate stage of succession. The Jaccard Similarity Coefficients showed that the intermediate and late successional stages had high similarity, whereas the early successional stage had low similarity with these two successional stages. Mortality rates were higher in the early stage, especially in stems with smaller diameters (5-10cm). This information contributes to the dissemination of important knowledge for the conservation of the tropical dry forests of Brazil, which are the most threatened ecosystems in this country and, at the same time, the least studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".