Factors controlling the spatial heterogeneity of soil organic carbon concentrations and stocks in a boreal forest
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Bibliographic record
Abstract
Boreal soils play a crucial role in the global terrestrial carbon cycle, serving as significant carbon reservoirs. However, this region is experiencing rapid climate change which poses a threat to the stability of the soil carbon pool. To understand the carbon balance in the boreal region, it is essential to obtain accurate estimates of soil organic carbon (SOC) stocks. The objective of this study was to investigate factors controlling the spatial heterogeneity of SOC concentrations and stocks in a boreal forested catchment and to quantify spatial variability of SOC concentrations and stocks using a random forest-based regression kriging approach. Soil carbon content, gravel content and bulk density were obtained from the surface soil layer (0–30 cm) of 106 samples across a 390 ha catchment in northwestern Ontario, Canada. Spatial attributes including elevation, wetness index, distance to nearest ridge, relative position index, plan curvature, profile curvature and normalized difference vegetation index were used in the random forest-based regression kriging to estimate spatial heterogeneity of SOC stocks and concentrations across the catchment. Our results showed that shallow soils in well-drained upland areas contained low to moderate SOC concentrations (1.2–16.2%) while depressional wetlands had high concentrations (19.2–54.0%). Topographic variables, including wetness index were the strongest predictors of SOC concentrations, but SOC stocks were very heterogeneous across the study site (12.9–139.0 Mg ha−1) and were not well explained by topographic or vegetation variables. The gravel content was highly variable (0–29.0%), and without accounting for this fraction, SOC stocks were overestimated by 52.5% in this landscape. These results emphasize the substantial catchment scale variability in SOC stocks in boreal landscapes and underscore the significance of considering both landscape-scale drivers and intrinsic soil properties when estimating soil carbon in this region. By understanding these factors, we can better manage and protect the important carbon stores in boreal soils.
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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 | 0.000 | 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 it