The effect of atmospheric turbulence and chamber deployment period on autochamber CO <sub>2</sub> and CH <sub>4</sub> flux measurements in an ombrotrophic peatland
Bibliographic record
Abstract
Abstract. Accurate quantification of soil-atmosphere gas exchange is essential for understanding the magnitude and controls of greenhouse gas emissions. We used an automatic, closed, dynamic chamber system to measure the fluxes of CO2 and CH4 for several years at the ombrotrophic Mer Bleue peatland near Ottawa, Canada and found that atmospheric turbulence and chamber deployment period had a considerable influence on the observed flux rates. With a short deployment period of 2.5 min, CH4 flux exhibited strong diel patterns and both CH4 and nighttime CO2 effluxes were highly and negatively correlated with ambient friction velocity as were the CO2 concentration gradients in the top 20 cm of peat. This suggests winds were flushing the very porous and relatively dry near-surface peat layers and reducing the belowground gas concentration gradient, which then led to flux underestimations owing to a decrease in turbulence inside the headspace during chamber deployment compared to the ambient windy conditions. We found a 9 to 57% underestimate of the net biological CH4 flux at any time of day and a 13 to 21% underestimate of nighttime CO2 effluxes in highly turbulent conditions. Conversely, there was evidence of an overestimation of ~ 100% of net biological CH4 and nighttime CO2 fluxes in calm atmospheric conditions possibly due to enhanced near-surface gas concentration gradient by mixing of chamber headspace air by fans. These problems were resolved by extending the deployment period to 30 min. After 13 min of chamber closure, the flux rate of CH4 and nighttime CO2 became constant and were not affected by turbulence thereafter, yielding a reliable estimate of the net biological fluxes. The measurement biases we observed likely exist to some extent in all chamber flux measurements made on porous and aerated substrate, such as peatlands, organic soils in tundra and forests, and snow-covered surfaces, but would be difficult to detect unless high frequency, semi-continuous observations were made.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".