Small-Scale Spatial Variability in Carbon Fluxes Driven by Soil and Vegetation Characteristics in Wetlands of Trail Valley Creek, Canada
Bibliographic record
Abstract
Abstract The microtopography of the Arctic tundra and the associated soil moisture (SM) gradient influence the net ecosystem‐atmosphere exchange of methane (CH 4 ) and carbon dioxide (CO 2 ). To quantify fine‐scale variability in a permafrost ecosystem, we measured growing‐season carbon fluxes with closed chambers at Trail Valley Creek, Canada from 2022 to 2024. A total of six landforms were sampled, spanning a wetness gradient from dry (upland tundra, gully) over intermediate (polygons, degraded wetland centers) to wet (transitional zones, trenches) microsites. All landforms were net sources of CH 4 ; only trenches had high (0.58 mg CH 4 m −2 h −1 ) fluxes, while the other landforms had fluxes close to zero. Drier elements (upland tundra, polygons) were net CO 2 sinks, while wetter depressions (gully, degraded centers, transitional zones) were net sources; trenches were a wet exception that still acted as a sink. All fluxes were strongly influenced by air temperature ( T air ), peaking during the hot summer of 2023. CH 4 flux variability was dominated by belowground variables (SM and temperature). For CO 2 fluxes, aboveground ( T air , and photosynthetically active radiation (PAR)) and belowground controls contributed equally. For CH 4 , ecosystem respiration, and gross primary production, fitting separate models per landform reduced absolute prediction error (despite lower R 2 ), making it preferable when minimizing error. For net ecosystem exchange, a single model fit to all landforms was sufficient. These results show that field studies focusing on small‐scale variability in carbon fluxes should prioritize detailed soil‐layer measurements, T air , and PAR, while vegetation metrics are optional.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".