Controls on spatial and temporal patterns of soil nitrogen availability in a High Arctic wetland
Bibliographic record
Abstract
Abstract. Increased soil nutrient availability, and associated increase in vegetation productivity, could create a negative feedback between Arctic ecosystems and the climate system, 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 how they vary over space and time. This study explores the environmental controls over spatial patterns of soil nitrogen availability in a High Arctic wet sedge meadow and how they influence carbon exchange processes. Ion exchange resin membranes measured available inorganic nitrogen in soils throughout the growing season at a high spatial resolution, while environmental variables (e.g. active layer depth, soil temperature, soil moisture) and carbon flux measurements were taken at frequent intervals during the 2016 field season. Environmental measures correlated highly with total and late season nitrate levels (total season dry tracks nitrate R2 = 0.533, total season wet tracks nitrate R2 = 0.803, late season nitrate R2 = 0.622), with soil temperatures at 5 cm depth having the greatest effect. Soil available nitrate and ammonium correlated highly with total and early season gross primary productivity (total season wet tracks R2 = 0.685, early season dry tracks R2 = 0.788, early season wet tracks R2 = 0.785). Higher ammonium concentrations coincided with greater carbon dioxide uptake. Nitrate concentrations correlated strongly to soil moisture, but nitrate levels were much lower than ammonium concentrations, suggesting low rates of nitrification vs. mineralization. Similar patterns were observed regardless of whether the wet-sedge meadow was classified as wet or dry, but the relationships were always stronger in areas classified as wet, indicating the importance of moisture and water availability on abiotic processes in High Arctic wet sedge meadows. Topography played an important role in the movement and transport of water, which influenced how nutrients were cycled and moved within the wetland. Generally, the low-lying areas had the highest inorganic nitrogen concentrations. These results suggest that finer scale processes altering nitrogen availability may influence the overall carbon balance of wet sedge meadows in the High Arctic, and how these ecosystems may respond to changes in climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".