Landscape-scale variability in soil organic carbon storage in the central Canadian Arctic
Bibliographic record
Abstract
Campeau, A. B., Lafleur, P. M. and Humphreys, E. R. 2014. Landscape-scale variability in soil organic carbon storage in the central Canadian Arctic. Can. J. Soil Sci. 94: 477-488. Arctic soils constitute a vast, but poorly quantified, pool of soil organic carbon (SOC). The uncertainty associated with pan-Arctic SOC storage estimates - a result of limited SOC and land cover data - needs to be reduced if we are to better predict the impact of future changes to Arctic carbon stocks resulting from climate warming. In this study landscape-scale variability in SOC at a Southern Arctic Ecozone site in the central Canadian Arctic was investigated with the ultimate goal of up-scaling SOC estimates with a land cover classification system. Total SOC was estimated to depths of 30 cm and 50 cm for 76 soil pits, together representing eight different vegetation communities in seven different broad landscape units. Soil organic carbon to 50 cm was lowest for the xerophytic herb community in the esker complex landscape unit (7.2±2.2 SD kg m-2) and highest in the birch hummock terrain in the lowland tundra landscape unit (36.4±2.8 kg m-2), followed by wet sedge and dry sedge communities in the wetland complex (29.8±9.9 and 22.0±2.0 kg m-2, respectively). The up-scaled estimates of mean SOC for the study area (excluding water) were 15.8 kg m-2 (to 50 cm) and 11.6 kg m-2 (to 30 cm). On a landscape scale, soil moisture content was found to have an important influence on SOC variability. Overall, this study highlights the importance of SOC variability at fine scales and its impact on up-scaling SOC in Arctic landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".