Temperature Sensitivity of Soil Greenhouse Gas Production in Three High‐Arctic Plant Community Types at Cape Bounty, Nunavut
Bibliographic record
Abstract
Enhanced precipitation and higher temperatures are expected in the Arctic as the result of future climatic warming. To understand future contributions of high‐arctic ecosystems to the climate system, we need to understand the feedbacks between climate and greenhouse gas production, and how they might vary between plant community types distributed along soil moisture gradients. We incubated intact soil cores in the laboratory to explore the temperature sensitivity of soil greenhouse gas (CO2, CH4, N2O) production across the three main plant community types of Cape Bounty, Nunavut: polar desert, mesic tundra and wet sedge. Two sets of cores (0‐10 cm mineral soil) were incubated in the laboratory at 4, 8, and 12°C for one month. We also measured plant community differences in soil thermal regimes for one year. Mean field temperatures were highest in the polar desert during the summer months, while temperatures in the mesic tundra were lowest during this time. In the winter, soil temperatures were lowest in the polar desert and highest in the wet sedge communities. Initial incubation results demonstrate Q10 values for CO2 production ranging from 2.18 in wet sedge to 8.67 in polar desert soils. We observed a Q10 of 4.03 for CH4 output in mesic tundra soils and a Q10 of 16.42 for N2O output in wet sedge soils. Our results suggest that use of single Q10 values to predict future greenhouse gas emissions from high‐arctic ecosystems would likely underestimate the contribution of these ecosystems to the global climate system in a warmer climate.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".