Biomass models for three species with different growth forms and geographic distribution in the Brazilian Atlantic forest
Bibliographic record
Abstract
Allometric models embedding independent variables such as diameter at breast height (d) and total height (h) are useful tools to predict the biomass of individual trees. Models for tropical forests are often constructed based on datasets composed of species with different morphological features and architectural models. It is reasonable to expect, however, that species-specific models may reduce uncertainties in biomass predictions, especially for palms, tree ferns, and trees with peculiar morphological features, such as stilt roots and hollow trunks. In this sense, three species with wide geographical distribution in the Brazilian Atlantic Forest were sampled, namely Euterpe edulis Mart., Cyathea delgadii Sternb., and Cecropia glaziovii Snethl., with the aim to (i) quantify their aboveground biomass (AGB), (ii) evaluate the AGB distribution in different plant compartments, (iii) fit species-specific models for predicting AGB at the individual level, and (iv) assess the performance of specific and generic models available in the literature to predict the AGB of individuals of these species. The compartment stem represented, on average, ∼74% of the total AGB of E. edulis individuals; in turn, the caudex compartment of C. delgadii represented, on average, ∼87% of the total AGB, while the trunk compartment of C. glaziovii represented, on average, ∼74%. Among the fitted models, the power model [Formula: see text] showed the best performance for E. edulis and C. delgadii. In turn, the asymptotic logistic model [Formula: see text], where d c is the diameter above the upper stilt root, presented the best performance for C. glaziovii. The variable h appeared as the most important predictor of AGB of E. edulis and C. delgadii; in contrast, the stem and caudex mean basic specific gravities were not suitable predictors. The fitted species-specific models outperformed the specific and generic models selected from the literature. They may, therefore, contribute to the reduction of uncertainties in AGB estimates. In addition, the results support evidence that specific models may be necessary for species with different growth forms and (or) peculiar morphological features, especially those with great abundance and wide geographic distribution.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".