Identifying Spatial Patterns in Greenhouse Gas Fluxes through an Arctic Tundra Snowpack 
Bibliographic record
Abstract
Cold season greenhouse gas (GHG) emissions have been found to make non-negligible contributions to annual carbon budgets in Arctic-boreal regions. The Arctic is warming three to four times faster than the global average, changing the magnitude and phase (snow/rain) of precipitation, and the thermal regimes of snow-covered ground.Future projections of winter GHG emissions require accurate simulations of the insulative properties of Arctic snowpacks and improved parameterisations of soil heterotrophic respiration as a function of soil thermal and moisture regimes. To improve these parameterisations in terrestrial biospheric models, we measured carbon dioxide and methane fluxes through the late-winter snowpack of a mineral upland tundra site in the western Canadian Arctic. Fluxes were calculated using highly resolved GHG snow concentration gradients and vertical snowpack microstructure (n = 119), over a range of microtopographic and vegetation types.GHG emission rates were statistically independent of vertical snow microstructures, suggesting high snow gas porosity relative to soil emission. Carbon dioxide emissions were measured across a wide range of tundra landscape types, and were closely linked to soil temperatures, vegetation type and snow depths. Importantly, persistent net methane sinks were also found across landcover types in warmer soils (-6 to -2 oC), showing active methane oxidation during winter periods. Methane emissions were not always consistent within surface cover types, suggesting available liquid soil moisture and carbon availability as important controls.
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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 | 0.000 | 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) | 0.000 | 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".