Reduced CO<sub>2</sub> Release from Arctic Soils Due to CO<sub>2</sub> Binding to Calcium Forming Aragonite
Bibliographic record
Abstract
Arctic soils are the largest pool of organic carbon compared with other soils globally and serve as a main source for greenhouse gases, especially in the course of the predicted future temperature increase. With increasing temperatures, substantial thawing of the permafrost layer of soils is expected, altering the availability of calcium in those soils, with an increase by ∼5 mg Ca g –1 DW predicted for Alaska. Here we show for two representative soils in Alaska (initially Ca-poor or Ca-rich) that this increase in Ca availability will lead to decreases in CO 2 release by 50% and 57%. It is already well-known that the cation bridging of Ca ions to organic carbon renders this carbon unavailable for microbial respiration and that Ca is altering the transformation of C org by microbes. Here we show that the decrease of the soil CO 2 release may be also due to enhanced aragonite formation (by 300% for Ca-poor and 90–200% for Ca-rich soils), as revealed by synchrotron-based scanning transmission X-ray microscopy. We therefore call upon field experiments for validation of this process and inclusion of this process in global and local carbon budget models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".