Reduced Net CO<sub>2</sub> Uptake During Dry Summers in a Boreal Shield Peatland
Bibliographic record
Abstract
Abstract Peatlands are globally important long‐term sinks of atmospheric carbon dioxide (CO 2 ). However, there is concern that climate change‐mediated drying will reduce gross primary productivity (GPP) and increase ecosystem respiration (ER) making peatlands vulnerable to a weaker carbon sink function and potential net carbon loss. While large and deep peatlands are usually resilient to moderate summer drying, CO 2 exchange in shallow Boreal Shield peatlands is likely more sensitive to drying given the reduced groundwater connectivity and water storage potential. To better understand the carbon cycling responses of Boreal Shield peatlands to meteorological conditions, we examined ecohydrological controls on CO 2 fluxes using the eddy covariance technique at a shallow peatland during the summer season for 5 years, from 2016–2020. We found lower GPP in dry summer years. Mean summer water table depth (WTD) was found to be significantly correlated with summer total net ecosystem CO 2 exchange ( R 2 = 0.78; p value = 0.046) and GPP ( R 2 = 0.83; p value = 0.03), where wet summers with a WT close to the peat surface sequestered more than twice the amount of CO 2 than dry summers. Our findings suggest that shallow Boreal Shield peatland GPP may be sensitive to climate‐mediated drying as they may switch to a net CO 2 source in the summer season when WTDs exceed a critical ecohydrological threshold for a prolonged period of time.
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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".