Towards Resolving Spatial and Temporal Greenhouse Gas Dynamics across a Heterogeneous Arctic Tundra Landscape in the Western Canadian Arctic
Bibliographic record
Abstract
The Arctic is currently warming faster than the rest of the world. Warming and associated permafrost thaw in Arctic landscapes may mobilize large pools of carbon (C) and nitrogen (N) and ultimately increase the atmospheric burden of the greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Arctic GHG dynamics and their environmental and hydrological controls are poorly understood. Whether Arctic landscapes act as a net GHG source or sink depends on the complex and spatially varying interactions between hydrology, active layer thickness, topography, temperature, vegetation, substrate availability and microbial dynamics. Our study site, Trail Valley Creek (68°44’ N, 133°29’ W), is an upland tundra site characterized by small-scale (<10 m) land cover and soil type (mineral and organic) heterogeneity consisting of different land cover types: shrub, tussock and lichen patches, polygonal tundra and thermokarst-affected areas, wetlands, lakes, and streams. To understand the large spatial and temporal variability of GHG dynamics across these terrestrial and aquatic landcover types we use a nested observational approach at plot- (<1 m2), ecosystem- (~10 m2), landscape- (~100 m2) and regional (~50 km2) scale. Existing (since 2013) ecosystem-scale eddy covariance (EC) measurements of net CO2 and CH4 exchanges are complemented with landscape-scale EC measurements and plot-scale automated and manual chamber measurements within the EC tower footprint and beyond. To increase process-based understanding we complement these multi-scale GHG flux observations with a wide array of auxiliary measurements including soil profile dynamics of CO2, CH4, N2O, and oxygen, lake and soil pore nutrient concentrations, soil temperature and moisture profiles, thaw depth, leaf area index (LAI), normalized difference vegetation index (NDVI), lake catchment characteristics, and quality and microbial degradability of aquatic dissolved organic matter. Preliminary results from manual chamber measurements show that tussocks were the largest net CO2 sink during the growing season. While the majority of terrestrial landcover types showed small but consistent and seasonally varying CH4 uptake, lake shore and thermokarst-affected areas displayed high nutrient loads and were hotspots of CH4 emissions. Therefore, capturing the landscape heterogeneity, areal coverage and hydrological connectivity of terrestrial and aquatic landcover types is important and our study highlights the need to combine belowground, plot-, ecosystem- and landscape-scale measurements to understand biosphere-atmosphere interactions in the Arctic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".