Temperature, moisture and freeze–thaw controls on CO2 production in soil incubations from northern peatlands
Bibliographic record
Abstract
Abstract Peat accumulation in high latitude wetlands represents a natural long-term carbon sink, resulting from the cumulative excess of growing season net ecosystem production over non-growing season (NGS) net mineralization in soils. With high latitudes experiencing warming at a faster pace than the global average, especially during the NGS, a major concern is that enhanced mineralization of soil organic carbon will steadily increase CO2 emissions from northern peatlands. In this study, we conducted laboratory incubations with soils from boreal and temperate peatlands across Canada. Peat soils were pretreated for different soil moisture levels, and CO2 production rates were measured at 12 sequential temperatures, covering a range from − 10 to + 35 °C including one freeze–thaw event. On average, the CO2 production rates in the boreal peat samples increased more sharply with temperature than in the temperate peat samples. For same temperature, optimum soil moisture levels for CO2 production were higher in the peat samples from more flooded sites. However, standard reaction kinetics (e.g., Q10 temperature coefficient and Arrhenius equation) failed to account for the apparent lack of temperature dependence of CO2 production rates measured below 0 °C, and a sudden increase after a freezing event. Thus, we caution against using the simple kinetic expressions to represent the CO2 emissions from northern peatlands, especially regarding the long NGS period with multiple soil freeze and thaw events.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".