Coarse woody debris dynamics in a secondary Atlantic Forest fragment in Brazil
Bibliographic record
Abstract
The Atlantic Forest fragments have suffered from the impacts of climate change, resulting in the increased production of coarse woody debris (CWD), which needs to be evaluated in space and time to generate accurate estimates of carbon accumulation. Thus, the goals of this study were (i) to quantify the CWD volume, necromass, carbon stock, and annual increment of carbon (AIcarb) over a period of 4 years; and (ii) to select the optimal combination of climatic, topographic, edaphic, and intrinsic forest variables to accurately predict AIcarb using machine learning and multivariate analysis. The CWD volume, necromass, and carbon stock increased between 2017 and 2020. The AIcarb was 1.09 MgC ha−1 year−1 (2017–2018), 1.24 MgC ha−1 year−1 (2018–2019), and 2.31 MgC ha−1 year−1 (2019–2020). Statistical analysis indicated that climate variables had greater weight in the CWD carbon increment in the 2018–2019 and 2019–2020 periods, while edaphic, topographical, and intrinsic forest variables were more important for the 2017–2018 period. Our findings showed that the carbon increase in CWD was linked to temporal and spatial variables within forests. These results demonstrate the importance of this parameter in the carbon cycle of forest ecosystems and highlight that there should be greater international research efforts to quantify this carbon pool.
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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".