A well‐established fact: Rapid mineralization of organic inputs is an important factor for soil carbon sequestration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We have read with interest an opinion paper recently published in the European Journal of Soil Science (Berthelin et al., 2022). This paper presents some interesting considerations, at least one of which is already well known to soil scientists working on soil organic carbon (SOC), that is, a large portion (80%–90%) of fresh carbon inputs to soil is subject to rapid mineralization. The short‐term mineralization kinetics of organic inputs is well‐known and accounted for in soil organic matter models. Thus, clearly, the long‐term predictions based on these models do not overlook short‐term mineralization. We point out that many agronomic practices can significantly contribute to SOC sequestration. If conducted responsibly whilst fully recognising the caveats, SOC sequestration can lead to a win‐win situation where agriculture can both contribute to the mitigation of climate change and adapt to it, whilst at the same time delivering other co‐benefits such as reduced soil erosion and enhanced biodiversity. Highlights Rapid mineralization of organic inputs is an important factor for soil carbon sequestration. Mineralization kinetics of organic inputs are well‐known and accounted for in soil organic matter models. Many agronomic practices can contribute significantly to SOC sequestration. SOC sequestration can lead to a win‐win situation where agriculture can both contribute to the mitigation of climate change and adapt to it.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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