VEGETATIVE PHENOLOGY IN CERRADO FOREST VEGETATION TYPES: A COMPARATIVE APPROACH
Bibliographic record
Abstract
The Cerrado vegetation types has the same macroclimate, but differs in the microclimatic and edaphic proprieties, which can result in contrasting phenologies among it.The vegetative phenology of five vegetation types such as woody savanna, deciduous and semideciduous forests, gallery forest, and riparian forest in Brazil Central plateau were accessed in this study.Leaf falls and leaf budding of 10 individuals of the 12 most abundant species were evaluated monthly in each vegetation type over one year.Circular statistics and the Rayleigh test were used to assess the phenology peaks.The average intensity rate of leaf fall and leaf budding were calculated for each vegetation type and chi-square test was used to analyse differences among them.Of the 53 species studies, 15 were deciduous, 17 semideciduous and 21 evergreens.All vegetation types demonstrated seasonality, reflecting the season macroclimate predominance.However, the peaks of phenophases were different between them throughout the year, which in turn reflects the effect of the microclimatic and edaphic proprieties.Our results indicated that within the same remnant a greater number of forests patches may increase phenological strategies.Thus, several vegetation types ensured resources for associated fauna and ecosystem services at different periods of the year.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".