Soil Nitrogen Dynamics Impact Carbon Exchange Processes in a High Arctic Wetland
Bibliographic record
Abstract
Abstract Increased soil nutrient availability, and associated increases in vegetation productivity, could create a negative feedback between Arctic ecosystems and the climate system, thereby reducing the contribution of Arctic ecosystems to future climate change. To predict whether this feedback will develop, it is important to understand the environmental controls over nutrient cycling in High Arctic ecosystems and their impact on carbon cycling processes. This study, conducted at the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut, examined the environmental controls over soil nitrogen availability in a High Arctic wet sedge meadow and how they influenced carbon dioxide exchange processes from 2016-2018. Moisture variability across a seemingly homogenous wet sedge meadow allowed us to investigate nutrient availability and carbon dioxide exchange across naturally occurring moisture gradients over three growing seasons. The nature of the relationships (i.e., trends) between variables was consistent over the three years, but their magnitudes varied depending on climate conditions. Soil nitrogen availability, particularly ammonium, was higher in warmer years and wetter conditions and correlated positively with gross primary production (R 2 = 0.97) and net carbon dioxide uptake (R 2 = 0.88). Drier areas within the wetland had more nitrate availability, and this correlated negatively with net carbon dioxide exchange. Projections of a warmer, wetter Arctic and increased nutrient availability due to higher soil organic matter turnover suggest that northern wetlands will remain strong carbon dioxide sinks, or become stronger sinks, contributing to a negative feedback on the climate system.
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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 | 0.000 | 0.000 |
| 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".