Forest carbon management strategies influence storage compartmentalization in <i>Nothofagus antarctica</i> forest landscapes
Bibliographic record
Abstract
Silvopastoral systems are one of the strategies proposed to manage natural forests in southern Patagonia for livestock and timber purposes. In the context of climate change, it is necessary to design new management proposals to improve forest carbon sequestration. The objective was to quantify the innate carbon stocking (t C ha−1) variation in Nothofagus antarctica forests under natural dynamics in even- and uneven-aged structures, and in harvested and transformed stands. Carbon stocks were sampled in 145 forest stands, identifying 14 different components in above- and belowground strata. Results showed that the carbon content of the stands varied significantly with age (e.g., C contribution of different tree components), ranging from 289 to 386 t C ha−1. Deadwood was the variable that varied most among the successional stages. In harvested stands, carbon content changed significantly with increasing harvesting intensity (from 84.6% to 55.7%) and was lower than in non-harvested stands. These changes were reflected in reduced carbon accumulation in trees, deadwood, and soil layer and increased accumulation in understory plants. Silvopastoral system management can achieve a balance between productive objectives and maintenance of carbon stocks in managed forests, resulting in higher resilience and lower carbon losses, thus promoting sustainable forest management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".