Fuel treatment effects on stand-level carbon pools, treatment-related emissions, and fire risk in a Sierra Nevada mixed-conifer forestPublication No. 143 of the National Fire and Fire Surrogate Project.
Bibliographic record
Abstract
Policies have been enacted to encourage carbon (C) sequestration through afforestation, reforestation, and other silvicultural practices; however, the effects of wildfires on forest C stocks are poorly understood. We present information from Sierran mixed-conifer forests regarding how control, mechanical, prescribed-fire, and mechanical followed by prescribed-fire treatments affected C pools. Secondly, we report CO 2 emissions from machinery and burning associated with the treatments. Lastly, the effects of treatments on the potential for C loss to wildfire are presented. The amount of aboveground C in live trees was significantly reduced in mechanical-only and mechanical plus fire treatments; C contained in dead trees was not significantly different. There was no significant difference in aboveground live and dead tree C between the fire-only and control treatments. Fire-only and mechanical plus fire treatments emitted significantly more CO 2 than the mechanical treatment and control. Modeling results for the control demonstrated 90% of the live tree C had a high (>75%) chance of being killed in a wildfire; in contrast, all three active treatments had low vulnerabilities to C loss. With wildfire severity increasing in most Sierran forests, management actions designed to increase fire resistance are justified for long-term C sequestration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".