Modeling of tree height–diameter relationships in the Atlantic Forest: effect of forest type on tree allometry
Bibliographic record
Abstract
Tree height is one of the most important variables for quantitative assessment of forest stocks, but it is difficult to directly measure. Such allometric relationships of trees can vary between geographical regions, however, mainly due to climatic, edaphic, and floristic gradients. Based on the hypothesis that different forest types influence the generic modeling of tree height–diameter relationships on geographical scales, this study aimed to (i) fit equations to estimate tree height in Atlantic Forest types in the state of Rio de Janeiro, Brazil; (ii) compare efficiency and precision between generic and specific equations for forest types; and (iii) test the effect of different forest types and species on the height–diameter relationship. Four allometric models were tested for all forests (generic) and three main forest types (specific). Effects of tree size, forest types, and species on tree height estimation were analyzed using multiple linear models and mixed-effect linear models. A significant effect of forest type and species on tree height was seen, showing the need to apply local specific equations to minimize the effects that are not captured by generic equations. Differences in tree allometry between forest types were associated with temperature, rainfall, soil, and forest structure. These results confirm the effect of the local environment on the height–diameter relationship of trees as found over large scales in tropical forests.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".