Using Relationships Between Vegetation and Surface Soil Biogeochemical Properties to Assess Regional Soil Carbon Inventories for South Baffin Island, Nunavut, Canada
Bibliographic record
Abstract
Abstract As Arctic regions warm rapidly, it is unclear whether high‐latitude soil carbon (C) will decrease or increase. Predicting future dynamics of Arctic soil C stocks requires a better understanding of the quantities and controls of soil C. We explore the relationship between vegetation and surface soil C in an understudied region of the Arctic: Baffin Island, Nunavut, Canada. We combined soil C data for three vegetation types—polar desert, mesic tundra, and wet meadow—with a vegetation classification to upscale soil C stocks. Surface soil C differed significantly across vegetation types, and interactions existed between vegetation type and soil depth. Polar desert soils were consistently mineral, with relatively thin organic layers, low percent C, and high bulk density. Mesic soils exhibited an organic‐rich epipedon overlying mineral soil. Wet meadows were consistently organic soil with low bulk density and high percent C. For the top 20 cm, polar desert contained the least soil C (2.17 ± 0.48 kg m−2); mesic tundra had intermediate C (8.92 ± 0.74 kg m−2); wet meadow stored the most C (13.07 ± 0.69 kg m−2). Extrapolating to the top 30 cm, our results suggest that approximately 44 Tg C is stored in the study region with a mean landscape soil C stock of 2.75 kg m−2 for non‐water areas. Combining vegetation mapping with local soil C stocks considerably narrows the range of estimates from other upscaling approaches (27–189 Tg) for soil C on South Baffin Island.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".