Soil organic carbon pools in a periglacial landscape: a case study from the central Canadian Arctic
Bibliographic record
Abstract
Abstract We investigated total storage and landscape partitioning of soil organic carbon (SOC) in continuous permafrost terrain, central Canadian Arctic. The study is based on soil chemical analyses of pedons sampled to 1‐m depth at 35 individual sites along three transects. Radiocarbon dating of cryoturbated soil pockets, basal peat and fossil wood shows that cryoturbation processes have been occurring since the Middle Holocene and that peat deposits started to accumulate in a forest‐tundra environment where spruce was present (∼6000 cal yrs BP). Detailed partitioning of SOC into surface organic horizons, cryoturbated soil pockets and non‐cryoturbated mineral soil horizons is calculated (with storage in active layer and permafrost calculated separately) and explored using principal component analysis. The detailed partitioning and mean storage of SOC in the landscape are estimated from transect vegetation inventories and a land cover classification based on a Landsat satellite image. Mean SOC storage in the 0–100‐cm depth interval is 33.8 kg C m−2, of which 11.8 kg C m−2 is in permafrost. Fifty‐six per cent of the total SOC mass is stored in peatlands (mainly bogs), but cryoturbated soil pockets in Turbic Cryosols also contribute significantly (17%). Elemental C/N ratios indicate that this cryoturbated soil organic matter (SOM) decomposes more slowly than SOM in surface O‐horizons. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".