Wind as a Driver of Peat CO2 Dynamics in a Northern Bog
Bibliographic record
Abstract
Abstract Excess CO2 accumulated in soils is typically transported to the atmosphere through molecular diffusion along a concentration gradient. Because of the slow and constant nature of this process, a steady state between peat CO2 production and emissions is often established. However, in peatland ecosystems, high peat porosity could foster additional non-diffusive transport processes, whose dynamics may become important to peat CO2 storage, transport and emission. Based on a continuous record of in situ peat pore CO2 concentration within the unsaturated zone of a raised bog in southern Canada, we show that changes in wind speed create large diel fluctuations in peat pore CO2 store. Peat CO2 builds up overnight and is regularly flushed out the following morning. Persistently high wind speed during the day maintains the peat CO2 with concentrations close to that of the ambient air. At night, wind speed decreases and CO2 production overtakes the transport rate leading to the accumulation of CO2 in the peat. Our results indicate that the effective diffusion coefficient fluctuates based on wind speed and generally exceeds the estimated molecular diffusion coefficient. The balance between peat CO2 accumulation and transport is most dynamic within the range of 0–2 m s−1 wind speeds, which occurs over 75% of the growing season and dominates night-time measurements. Wind therefore drives considerable temporal dynamics in peat CO2 transport and storage, particularly over sub-daily timescales, such that peat CO2 emissions can only be directly related to biological production over longer timescales.
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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".