Bibliographic record
Abstract
The carbon (C) dynamics of northern peatlands are sensitive to hydrological changes owing to ecohydrological feedback. We quantified and evaluated the impact of water level variations in a beaver pond (BP) on the CO2 flux dynamics of an adjacent, raised Sphagnum – shrub-dominated bog in southern Canada. We applied the CoupModel to the Mer Bleue bog, where the hydrological, energy and CO2 fluxes have been measured continuously for over 20 years. The lateral flow from the bog to the BP was estimated by the hydraulic gradient between the peatland and the BP's water level and the vertical profile of peat hydraulic conductivity. The model outputs were compared with the measured hydrological components, CO2 flux and energy flux data (1998–2019). CoupModel was able to reproduce the measured data well. The simulation shows that variation in the BP water level (naturally occurring or due to management) influenced the bog net ecosystem exchange of CO2 (NEE). Over 1998–2004, the BP water level was 0.75 to 1.0 m lower than during 2017–2019. Simulated net CO2 uptake was 55 g C m−2 yr−1 lower during 1998–2004 compared to 2017–2019 when there was no BP disturbance, which was similar to the differences in measured NEE between those periods. Peatland annual NEE was well correlated with water table depth within the bog, and NEE also shows a linear relation with the water level at the BP, with a slope of −120 g CO2-C m−2 yr−1 m−1. The current modelling predicts the bog may switch from CO2 sink to source when the BP water levels drop lower than ~ 1.7 m below the peat surface at the eddy covariance tower, 250 m from the BP. This study highlights the importance of natural and human disturbances to adjacent water bodies in regulating net CO2 uptake function of northern peatlands.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.317 | 0.243 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".