Soil Nitrogen Transformations Associated with Small Patterned‐Ground Features along a North American Arctic Transect
Bibliographic record
Abstract
ABSTRACT Small patterned‐ground features (PGFs) in the Arctic have unique soil properties that vary with latitude and may greatly affect tundra biogeochemistry. Because nitrogen availability can strongly limit arctic vegetation growth, we examined how soil nitrogen transformations differ between PGFs and the surrounding inter‐PGF tundra along an arctic latitudinal gradient. We collected soils at eight sites from the Alaskan Low Arctic to the Canadian High Arctic. The soils were incubated for 21 days at 9 °C and 15 °C and analysed for changes in total inorganic nitrogen, nitrate and extractable organic nitrogen (EON). We found greater nitrogen immobilisation in the surrounding inter‐PGF soils than in the PGF soils. Along the latitudinal gradient, differences in net nitrogen mineralisation and EON cycling between PGF and inter‐PGF soils were strongly influenced by the presence of a pH boundary within the Low Arctic and the transition between the High and Low Arctic, with greater immobilisation in the nonacidic and Low Arctic sites, respectively. Incubation temperature affected EON flux but did not affect net nitrogen mineralisation or nitrification. These results show that spatial heterogeneity at several scales can influence soil nitrogen dynamics, and is therefore an important influence on arctic ecosystem function. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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".