“Reactive Mineral Sink” drives soil organic matter dynamics and stabilization
Bibliographic record
Abstract
Abstract Reactive primary and secondary minerals play a critical role in the transformation and stabilization of organic matter (OM) in soil, a critical aspect that has been largely overlooked in existing literature. In this regard, we propose a new model known as the “reactive mineral sink” (RMS) to illustrate three primary mechanisms through which these minerals drive the bioprocessing, transformation, transport and stabilization of OM in soil. Firstly, from a biological perspective, reactive minerals influence enzymatic and microbial OM processing through binding enzymatic proteins or influencing the structure of microbial communities. Secondly, from a chemical standpoint, these minerals have the capacity to adsorb OM and/or coprecipitate with it, leading to a more diverse distribution of OM in the soil. This distribution, in turn, triggers OM transformation through chemical catalysis and redox reactions. Thirdly, on a physical level, reactive minerals have a substantial impact on soil architecture, aggregate dynamics, porosity development, and hydrological processes. These physical changes then affect the transport, reprocessing and stabilization of OM. The RMS model provides a conceptual framework that underscores the fundamental role of reactive minerals in driving the dynamics of OM and carbon (C) sequestration in natural soil. Furthermore, it promotes the restoration of soil biogeochemical processes and ecological resilience. We advocate for the implementation of strategies based on the RMS model to enhance the sequestration of organic C in soils for the purposes of rejuvenating soil health and mitigating CO2 emission.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".