High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
Bibliographic record
Abstract
Soil organic carbon (SOC) in Arctic coastal polygonal tundra is vulnerable to climate change, especially in soils with occurrence of large amounts of ground ice. Pan-arctic studies of mapping SOC exist, yet they fail to describe the high spatial variability of SOC storage in permafrost landscapes. An important factor is the landscape history which determines landform development and consequently the spatial variability of SOC. Our aim was to map SOC stocks, and which environmental variables that determine SOC, in two adjacent coastal areas along Canadian Beaufort Sea coast with different glacial history. We used the machine learning technique random forest and environmental variables to map the spatial distribution of SOC stocks down to 1 m depth at a spatial resolution of 2 m for depth increments of 0–5, 5–15, 15–30, 30–60 and 60–100 cm. The results show that the two study areas had large differences in SOC stocks in the depth 60–100 cm due to high amounts of ground ice in one of the study areas. There are also differences in variable importance of the explanatory variables between the two areas. The area low in ground ice content had with 66.6 kg C/m−2 more stored SOC than the area rich in ground ice content with 40.0 kg C/m−2. However, this SOC stock could be potentially more vulnerable to climate change if ground ice melts and the ground subsides. The average N stock of the area low in ground ice is 3.77 kg m−2 and of the area rich in ground ice is 3.83 kg m−2. These findings support that there is a strong correlation between ground ice and SOC, with less SOC in ice-rich layers on a small scale. In addition to small scale studies of SOC mapping, detailed maps of ground ice content and distribution are needed for a validation of large-scale quantifications of SOC stocks and transferability of 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 | 0.001 |
| 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 | 0.000 |
| 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".