Temporal shifts in controls over methane emissions from a boreal bog
Bibliographic record
Abstract
We measured year-round landscape-scale methane (CH4) flux in a boreal bog from May 2014 to April 2016 using the eddy covariance technique. The objectives of the study were to investigate the controls on CH4 flux at different periods of the growing season and to quantify the annual CH4 flux budget. The daily average growing season water table (WT) ranged from -0.33 to −0.08 m in 2014 and from −0.36 to −0.08 m in 2015. Strong seasonal variability in the daily average CH4 fluxes was observed in both 2014 and 2015, ranging from near zero before May to a peak of above 20 nmol m−2 s−1 in the middle-late August in 2014 and in the early-middle September in 2015. Soil temperature at 50 cm and water table exerted interactive impact on the seasonal variation in the daily average growing season CH4 flux in both years. Soil temperature at 1 cm was negatively related to CH4 flux when water table dropped more than 0.25 m below the peat surface in 2015 growing season, suggesting that the seasonal variation in CH4 flux was dominated by the variation due to CH4 oxidation. During the non-growing season, the daily variation in CH4 fluxes was mostly related to friction velocity in both years. In addition, daily average CH4 flux was linearly related to net ecosystem exchange of CO2 (NEE) when daily NEE was negative (i.e., days with CO2 uptake larger than ecosystem respiration), but there was no correlation between them when NEE was positive (days with ecosystem respiration dominated over CO2 uptake) during the growing season. We found that this boreal bog acted as a small CH4 source of 3.7 ± 0.9 g CH4 m−2 from May 2014 to April 2015 and 3.1 ± 0.9 g CH4 m−2 from May 2015 to April 2016. These values were at the lower end of the range of CH4 emission rates reported for boreal peatlands. Non-growing season CH4 emissions accounted for 41% (the first study year) and 39% (the second study year) of the annual emissions, highlighting the importance of non-growing season CH4 emissions in estimating the annual CH4 budget and the feedback to climate.
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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 | 0.000 | 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 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".