Carbon availability in soils of thermo-erosional valleys– a case study from a valley on Herschel Island,West Canadian Arctic
Bibliographic record
Abstract
Permafrost is a perennially frozen ground often occurring in periglacial environments. Due to its frozen state, organic carbon accumulates in the soils. By temperature rise and thaw of the active layer, these stocks become vulnerable to microbial decomposition. To predict the future of organic carbon in the Arctic, it is necessary to expand the knowledge on its spatial distribution across arctic environments. This study examined the spatial distribution of organic carbon and its availability within a valley, which is subjected to thermo-erosion on Herschel Island, Yukon Territory. By analyses of soil samples variations in soil organic carbon, total nitrogen and the carbon-to-nitrogen ratio (C/N) were investigated. Ecological units, hillslope position and distance to shore helped to identify spatial differences between sites. The analyses showed that highest values for soil organic carbon, total nitrogen and C/N occurred on uplands, followed closely of the values in the valley bed. On slopes the values of soil organic carbon, total nitrogen and C/N were lower. Further, differences of the soil organic carbon, total nitrogen and C/N stocks occurred across the valley locations with distance to the shore. Upstream the soil organic carbon, total nitrogen and C/N stocks were higher to those downstream. Sites on slopes and downstream are characterized by continuous surface disturbances due to permafrost degradation, thermo-erosion and hillslope processes. This study could demonstrate that even in local scales organic carbon stocks and its availability differs spatially depending on environmental parameters.
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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 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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".