Carbon content and allometric models to estimate aboveground biomass for forest areas under restoration
Bibliographic record
Abstract
Maximizing carbon sequestration is crucial to mitigate climate change and indicates that the restoration technique used was effective. To quantify carbon stock over time on areas being restored, suitable allometric equations are needed. These equations are lacking for the Atlantic forest, lacking even more for restoration sites, and rare for restoration areas implemented with the active method, technique often used to restore Atlantic forest areas. Thus, the objective of this study was 3‐fold. First, to provide an equation to estimate aboveground biomass for 5‐year‐old Atlantic forest under restoration implemented with the active method. Second, to determine carbon content for the branch, stem, and foliage pools for those areas. Third, to present biomass, carbon content, and carbon stock benchmarks for 5‐year‐old areas under restoration implemented with the active method. Three sites were sampled with nine plots each, measuring tree height and diameter. One subplot was established in each plot, and all trees within it were harvested and had fresh weight measured and samples were taken to the laboratory to dry weight and carbon content determination. Ten models estimating biomass were fitted and tested. Mean carbon content in foliage, branch, stem, and the weighted average were 44.8, 44.5, 45.8, and 45.3%, respectively. Mean biomass and carbon stock were 20.19 ± 0.146 Mg/ha and 9.73 Mg C/ha. We concluded that the equation that we provide is precise, and very necessary to estimate biomass for young restoration areas at the Atlantic forest.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".