Drought-induced peatland carbon loss exacerbated by elevated CO <sub>2</sub> and warming
Bibliographic record
Abstract
Extreme drought events are predicted to increase with climate change, yet their impacts on ecosystem carbon dynamics under warming and elevated carbon dioxide (eCO 2 ) remain unclear. In a peatland experiment with five warming treatments each under ambient carbon dioxide (aCO 2 ) and eCO 2 (+500 parts per million), a 2-month extreme drought in 2021 reduced net ecosystem productivity by 444.0 ± 65.8 and 736.6 ± 57.8 grams of carbon per square meter at +9°C under aCO 2 and eCO 2 , respectively—228.6 ± 56.8% and 381.9 ± 83.4% of the reduction at +0°C under aCO 2 . This exacerbation was driven by warming-induced water table decline, prolonged low water tables, and CO 2 -enhanced substrate availability through increased plant carbon inputs. Findings indicate that future climate will greatly amplify carbon loss during extreme drought, reinforcing positive carbon-climate feedbacks.
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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 | 0.000 |
| 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".