Annual grass invasions and wildfire deplete ecosystem carbon storage by >50% to resistant base levels
Bibliographic record
Abstract
Ecological disturbance can affect carbon storage and stability and is a key consideration for managing lands to preserve or increase ecosystem carbon to ameliorate the greenhouse gas problem. Dryland soils are massive carbon reservoirs that are increasingly impacted by species invasions and altered fire regimes, including the exotic-grass-fire cycle in the extensive sagebrush steppe of North America. Direct measurement of total carbon in 1184 samples from landscapes of this region that differed in invasion and wildfire history revealed that their impacts depleted soil carbon by 42-49%, primarily in deep horizons, which could amount to 24.8-29.0 Tg carbon lost across the >500,000 ha affected annually. Disturbance effects on soil carbon stocks were not synergistic, suggesting that soil carbon was lowered to a base-level ‘floor’, beneath which further loss was unlikely. Restoration and maintenance of resilient dryland shrublands/rangelands could stabilize soil carbon at magnitudes that are relevant to the global carbon cycle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.011 |
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".