A 6 year longitudinal study of post-fire woody carbon dynamics in California’s forests
Bibliographic record
Abstract
We examined the dynamics of aboveground forest woody carbon pools — live trees, standing dead trees, and down wood — during the first 6 years following wildfire across a wide range of conditions, which are characteristic of California forest fires. From repeated measurements of the same plots, we estimated change in woody carbon pools as a function of crown fire severity as indicated by a post-fire index, years since fire, pre-fire woody carbon, forest type group (hardwood vs. softwood), elevation, and climate attributes. Our analysis relied on 130 U.S. national forest inventory plots measured before and 1 year after fire, with one additional remeasurement within 6 years after fire. There was no evidence of net change in total wood carbon, defined for this study as the wood in standing trees larger than 12.7 cm diameter at breast height and down wood larger than 7.6 cm in diameter, over the post-fire period in any of the three severity classes. Stands that burned at low severity exhibited considerable shifts from live to standing dead and down wood pools. In stands that burned at moderate severity, live wood decreased significantly whereas no net change was detected in standing dead or down wood. High severity fire burning resulted in movement from standing dead to down wood pools. Our results suggest that the carbon trajectories for stand-replacing fires may not be appropriate for the majority of California’s forest area that burned at low to moderate severities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".