Decomposition in Extreme‐Rich Fens of Boreal Alberta, Canada
Bibliographic record
Abstract
Rich fens (minerotrophic peatlands with surface water pH > 5.5) have greater alkalinity and species richness than other boreal peatlands. We used short‐term laboratory incubations to quantify CO 2 and CH 4 production in peat from five extreme‐rich fens in Alberta. Carbon dioxide production rates averaged 48.29 ± 1.36 μmol CO 2 g organic matter −1 d −1 across sites and sampling events. Peat from all sites produced CH 4 during anaerobic incubations, leading to average anaerobic CH 4 production rates of 359.53 ± 138.7 nmol CH 4 g organic matter −1 d −1 However, methane frequently was consumed (oxidized) during aerobic incubations, leading to aerobic CH 4 consumption rates averaging 75.2 ± 63.7 nmol CH 4 g organic matter −1 d −1 across sites. Calculated rates of dissolved H 2 CO 3 + HCO 3 − production averaged 59.7 ± 13.4 μmol g organic matter −1 d −1 , suggesting that dissolved inorganic C is important to the overall C fluxes in these rich fens. Our results suggest that changing hydrologic conditions will influence the balance between methanogenesis and methanotrophy in rich fens, but that surface water chemistry, likely influenced by marl precipitation, also is important to decomposition. Rich fens are estimated to represent the most common wetland type in Alberta, and these peatland ecosystems could play an important role in trace gas emissions across boreal regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".