Tree diameter growth for three successional stages of Tropical Dry Forest in Minas Gerais, Brazil
Bibliographic record
Abstract
he tropical dry forests of Brazil are classified as the most threatened and disturbed ecosystems in the country. We estimate the diameter growth in three successional stages in the Mata Seca State Park, in Minas Gerais, Brazil, through annual measurement of all individuals with more than 5 cm of diameter at breast height in 18 permanent plots (6 plots for each succession stage) in the early, intermediate, and late successional stages, over a period of 5 years (2006-2011). With this information the annual diameter increments for each individual were calculated to determine the diameter increments per stage, plot, and diameter class. The results show the following annual increments for each stage of succession: early (5.02 mm/year), intermediate (2.55 mm/year), and late (1.91 mm/year). We found high similarity in incremental growth between the plots in the intermediate and late stages. The greatest increments in the early stage was in the 15-20 cm diameter class, in the intermediate stage in the 30-35 cm class, and in the late stage in the 45-50 cm class. The dominant species with the highest increments were Myracrodruon urundeuva (9.33 mm/year) and Mimosa hostilis (10.35 mm/year). Species with lower increments were mostly those of the late stage. The high diameter increment in the early stage and the differences we observed between stages were associated with species composition and biophysical factors that regulate the growth and structure of each forest.
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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".