Multi-century times-since-fire and prior fire interval determine biomass carbon stocks in obligate-seeder eucalypt woodlands
Bibliographic record
Abstract
Background Understanding the influence of fires on terrestrial carbon stocks is important for informing global climate models and underpinning land management-based carbon markets. Aims To quantify biomass carbon in south-western Australia’s Great Western Woodlands – the world’s largest extant Mediterranean-climate woodland – with time-since-fire and prior fire interval. Methods Plot-based measurement of live and dead tree and shrub size, woody debris volume and litter mass across a ~400-year chronosequence to calculate biomass carbon. Key results Biomass carbon increased with time-since-fire, reaching >65 Mg C ha−1, although the rate of increase declined in mature woodlands. Biomass carbon decreased after fire in these obligate-seeder woodlands, while a longer prior fire interval buffered carbon fluxes through retained large standing dead trees and fallen woody debris. Conclusions The current age class distribution of the ~95,000 km2 of eucalypt woodlands in the region may support ~0.453 Pg C. Further refinement of carbon estimates explicitly considering variation in woodland type and climate, a continuous woodland age distribution and soil carbon are required to underpin a carbon methodology. Implications Biomass carbon would be maximised by reducing the extent of bushfires impacting woodlands, focussing on existing mature stands that support the greatest carbon stocks.
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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 | 0.000 | 0.000 |
| 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".