The estimation of carbon budgets of frequently burnt tree stands in savannas of northern Australia, using allometric analysis and isotopic discrimination
Bibliographic record
Abstract
The stock, rates of sequestration and allocation of carbon were estimated for trees in 14 0.1-ha plots at Kapalga in Kakadu National Park, Northern Territory, using new allometric relationships of carbon stock to stem cross-sectional area and measured growth rates of trees. Carbon stocks of trees ranged from 12 to 58 t ha–1, with sequestration representing ~9% of the total stocks. More than half of the sequestered carbon is allocated to leaves and twigs and ~20% to wood. Only ~25% is retained in the live trees with leaf and twig fall accounting for 80%–84% of the total transfers to the environment. An alternative method of calculating sequestration rates from consideration of water use and carbon-isotope discrimination data had a close to 1 : 1 match with estimates from allometric relationships. We developed and applied algorithms to predict the impacts of fire on carbon stocks of live trees. This showed that the reduction in live carbon stocks caused by single fires increased with increasing intensity, but the impact was highly dependent on the tree stand structure.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".