The Distribution of Soil Carbon and Nitrogen Stocks Among Dominant Geomorphological Terrain Units in Qarlikturvik Valley, Bylot Island, Arctic Canada
Bibliographic record
Abstract
Abstract Soils of circumpolar regions store large amounts of carbon (C) and are a crucial part of the global C cycle. Yet, little is known about the distribution of soil C stocks among geomorphological terrain units of glacial valleys in the Arctic. Soil C and nitrogen (N) content for the top 100 cm of the dominant vegetated geomorphological terrain units (i.e., alluvial fans, humid polygons, mesic polygons) at Qarlikturvik Valley, Bylot Island, Canada have been analyzed. Soil C content was greatest in humid low‐center ice‐wedge polygons (82 kg m−2), followed by mesic flat‐center ice‐wedge polygons (40 kg m−2), and alluvial fan area (16 kg m−2), due to prevailing geomorphological processes, differences in vegetation and soil characteristics, as well as permafrost processes. Soil N content was greatest in humid polygons (4 kg m−2), followed by mesic polygons (2 kg m−2), and alluvial fan area (1 kg m−2). Vertically, C and N decreased with increasing depth except for a peak in C at depth in humid polygons, a likely result of past changes in vegetation cover. At Qarlikturvik Valley, which has a size of 121.7 km2, alluvial fans store 0.226 Tg organic C and humid and mesic polygons store 1.643 and 0.218 Tg organic C, respectively in the top 100 cm of soil. Findings like these are important to further constrain pan‐Arctic soil C and N stock estimates and thus climate models.
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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 | 0.002 | 0.002 |
| 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".