Tree species community spatial structure in a terra firme Amazon forest, Brazil
Bibliographic record
Abstract
All trees with diameter at breast height dbh ≥ 10.0 cm were stem-mapped in a "terra firme" tropical rainforest in the Brazilian Amazon, at the EMBRAPA Experimental Site, Manaus, Brazil.Specifically, the relationships of tree species with soil properties were determined by using canonical correspondence analyses based on nine soil variables and 68 tree species.From the canonical correspondence analyses, the species were grouped into two groups: one where species occur mainly in sandy sites, presenting low organic matter content; and another one where species occur mainly in dry and clayey sites.Hence, we used Ripley`s K function to analyze the distribution of species in 32 plots ranging from 2,500 m 2 to 20,000 m 2 to determine whether each group presents some spatial aggregation as a soil variations result.Significant spatial aggregation for the two groups was found only at over 10,000 m 2 sampling units, particularly for those species found in clayey soils and drier environments, where the sampling units investigated seemed to meet the species requirements.Soil variables, mediated by topographic positions had influenced species spatial aggregation, mainly in an intermediate to large distances varied range (≥ 20 m).Based on our findings, we conclude that environmental heterogeneity and 10,000 m 2 minimum sample unit sizes should be considered in forest dynamic studies in order to understand the spatial processes structuring the "terra firme" tropical rainforest in Brazilian Amazon.
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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".