Allometric Models to Estimate Aboveground Biomass of Individual Trees of Eucalyptus saligna Sm in Young Plantations in Ecuador
Bibliographic record
Abstract
(1) Background: Nature-based solutions (NbS), particularly through forest biomass, are crucial in mitigating climate change. While forest plantations play a critical role in carbon capture, the absence of species-specific biomass estimation models presents a significant challenge. This research focuses on developing allometric models to accurately estimate the aboveground biomass of Eucalyptus saligna Sm in Ecuador’s Lower Montane thorny steppe. (2) Methods: Conducted at the Tunshi Experimental Station of ESPOCH in Chimborazo, Ecuador, the research involved 46 trees to formulate biomass predictive models using both destructive and non-destructive methods. Sixteen generic models were tested using the ordinary least squares method. (3) Results: The most effective allometric equation for estimating six-year-old E. saligna biomass was Ln(B) = −0.952 + 1.97∗Ln(dbh), where B = biomass in kg/tree, and dbh = diameter at breast height in cm. This model represents a valuable contribution to improve biomass and carbon estimates in mitigation projects in Ecuador. (4) Conclusions: The tested models stand out for their simplicity, requiring only dbh as input, and demonstrate high accuracy and fit to contribute to the field of climate change mitigation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".