Geochemical Versus Climatic Controls on Soil Organic Carbon
Bibliographic record
Abstract
Abstract Soil texture and climate are considered the major controls on soil organic carbon (SOC) storage globally, and model simulations suggest that cooler regions of the planet will be more sensitive to SOC losses caused by climate warming. To investigate this pattern, we measured SOC and geochemical properties in surface (0–10 cm) mineral soil at 198 forested sites across the Boreal Plains, Boreal Shield, and Taiga Shield Ecozones in central Canada, where mean annual air temperature (MAT) ranged from −6.0 to +0.7°C. Across the five ecoregions, SOC was strongly related to soil organic matter (SOM) with SOC:SOM ratio of 0.47. Despite the substantial temperature gradient, we found that SOC was only weakly correlated with temperature, precipitation, and net primary productivity (NPP). Instead, SOC was strongly related ( r > 0.7) to soil geochemical properties with SOC increasing in finer textured soils that had higher concentrations of aluminum (Al) and iron (Fe) and lower silica (Si) content. To extend the climate and soil geochemistry gradient, we expanded the analysis to include soils from temperate forests in the Mixedwood Plains Ecozone of southeastern Canada as well as published data from natural shrublands and grassland sites spanning the Southern Hemisphere and found that the strong correlations between SOC, Al + Fe, and Si persisted. These data suggest that soil texture and geochemical properties provide protection to SOC, and relationships with geochemistry must be incorporated in Earth System Models to improve spatial prediction of SOC stocks and their sensitivity to climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".