Spatiotemporal variability in peatland subsurface methane dynamics
Bibliographic record
Abstract
Peatlands are large natural sources of atmospheric methane (CH4). While many studies have measured CH4 emissions to the atmosphere, less is known about the stock and residence time of subsurface CH4. In this study we examined dissolved CH4 concentration in near‐surface peatland pore waters of a poor fen near Québec City, Canada, in order to (1) investigate the variability in and potential controls on these concentrations and (2) combine measured dissolved CH4 concentration with estimated bubble CH4 stock and measured CH4 fluxes to estimate the mean residence time of subsurface CH4. Concentrations ranged from 1 to 450 μM during both study seasons. Depth profiles were generally consistent at one location within the peatland throughout the sampling period but varied between locations. Patterns with depth were not well correlated to pore water pH or EC; however, changes in CH4 concentration through time in the upper 30 cm were related to temperature and water table at some locations. Depth profiles taken at 2‐ to 5‐cm intervals revealed discrete concentration “spikes” which were often maintained throughout the season and are likely related to bubble CH4 dynamics. Estimated subsurface CH4 stocks indicate that even when relatively low bubble volume (5% of peat volume) is assumed, bubble CH4 accounted for greater than half of total stocks. Calculated mean residence times were 28–120 days. This implies that CH4 flux may lag changes in water table and temperature which happen on shorter timescales (hours or days). To improve our description of subsurface CH4 stocks, links between dissolved and bubble CH4 stocks and peatland CH4 residence time, coincident measurement of pore water CH4 concentrations, entrapped gas content and composition, diffusive CH4 flux, and ebullition are required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".