The Case for Digging Deeper: Soil Organic Carbon Storage, Dynamics, and Controls in Our Changing World
Bibliographic record
Abstract
Most of our terrestrial carbon (C) storage occurs in soils as organic C derived from living organisms. Therefore, the fate of soil organic C (SOC) in response to changes in climate, land use, and management is of great concern. Here we provide a unified conceptual model for SOC cycling by gathering the available information on SOC sources, dissolved organic C (DOC) dynamics, and soil biogeochemical processes. The evidence suggests that belowground C inputs (from roots and microorganisms) are the dominant source of both SOC and DOC in most ecosystems. Considering our emerging understanding of SOC protection mechanisms and long-term storage, we highlight the present need to sample (often ignored) deeper soil layers. Contrary to long-held biases, deep SOC—which contains most of the global amount and is often hundreds to thousands of years old—is susceptible to decomposition on decadal timescales when the environmental conditions under which it accumulated change. Finally, we discuss the vulnerability of SOC in different soil types and ecosystems globally, as well as identify the need for methodological standardization of SOC quality and quantity analyses. Further study of SOC protection mechanisms and the deep soil biogeochemical environment will provide valuable information about controls on SOC cycling, which in turn may help prioritize C sequestration initiatives and provide key insights into climate-carbon feedbacks.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".