Mathematical Modelling of Arctic Polygonal Tundra with <i>Ecosys:</i> 2. Microtopography Determines How CO<sub>2</sub> and CH<sub>4</sub> Exchange Responds to Changes in Temperature and Precipitation
Bibliographic record
Abstract
Abstract Differences of surface elevation in arctic polygonal landforms cause spatial variation in soil water contents (θ), active layer depths (ALD), and thereby in CO2 and CH4 exchange. Here we test hypotheses in ecosys for topographic controls on CO2 and CH4 exchange in trough, rim, and center features of low‐ and flat‐centered polygons (LCP and FCP) against chamber and eddy covariance (EC) measurements during 2013 at Barrow, Alaska. Larger CO2 influxes and CH4 effluxes were measured with chambers and modeled with ecosys in LCPs than in FCPs and in lower features (troughs) than in higher (rims) within LCPs and FCPs. Spatially aggregated CO2 and CH4 fluxes from ecosys were significantly correlated with EC flux measurements. Lower features were modeled as C sinks (52–56 g C m−2 yr−1) and CH4 sources (4–6 g C m−2 yr−1), and higher features as near C neutral (−2–15 g C m−2 yr−1) and CH4 neutral (0.0–0.1 g C m−2 yr−1). Much of the spatial and temporal variations in CO2 and CH4 fluxes were modeled from topographic effects on water and snow movement and thereby on θ, ALD, and soil O2 concentrations. Model results forced with meteorological data from 1981 to 2015 indicated increasing net primary productivity in higher features and CH4 emissions in some lower and higher features since 2008, attributed mostly to recent rises in precipitation. Small‐scale variation in surface elevation causes large spatial variation of greenhouse gas (GHG) exchanges and therefore should be considered in estimates of GHG exchange in polygonal landscapes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".