Snapshot of Carbon Distribution and Degradation in Arctic Valleys
Bibliographic record
Abstract
Thermo-erosional valleys (TEVs) and gullies are important but poorly studied components of the western Canadian and American Arctic. Through combined mechanical and thermal processes, TEVs incise the landscapes and modify the carbon stocks through their impact on soils, deeper permafrost layers, and water drainage. To better understand the impact of TEVs on the biogeochemical cycling of lowland permafrost terrains, we investigated the distribution of soil organic carbon (SOC) and total nitrogen (TN) in three TEVs located on Herschel Island, Yukon Territory (Canada). The objective of this study is to describe the state of carbon stocks in the active layer and permafrost along toposequences of the TEVs. We 1) describe the geomorphology of the three TEVs and 2) assess the distribution and spatial variability of SOC and TN in the TEVs. The estimated SOC and TN stock in the upper meter of soils of 43 pits located along and across the 3 TEVs was 26.3 ± 8.8 kg/m3 and 2.1± 0.6 kg/m3 per site, respectively. There was a large variability among sites, SOC stocks varied between 9.9 and 46.7 kg/m3 and TN stocks between 0.9 and 3.7 kg/m3. Along the TEVs, SOC and TN stocks were higher in sites located upstream of the TEVs compared to downstream sites. Across the TEVs, stocks of SOC and TN were highest at the sites located at the bottom of the TEVs and lowest on the convex portion of their slopes. The C/N and δ13C mean values showed significantly less degraded organic-matter at less disturbed sites in the upper level of the TEVs. This study highlights the importance of thermo-erosional valleys on SOC stocks in Arctic landscapes.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".