Mineral – Organic Carbon interactions in Arctic permafrost: spatialization and meta-analysis
Bibliographic record
Abstract
The northern permafrost’s extent covers 21 million square kilometers which represents 22% of the Northern Hemisphere’s exposed land area. Within these 22%, it is estimated that 1460 to 1600 GT of organic carbon (OC) are stored. This estimation is yet growing as new research refines and adds new components to existing studies. With current climate change - occurring at a greater rate in the Arctic regions than the global average - there is a crucial need to further investigate the possible fates of this OC. A key element to consider in this regard concerns OC-mineral interactions. Among the mechanisms of OC-mineral interactions, OC complexation with metals is one of the main mechanisms and constitutes a pool of organic carbon that is not directly accessible to microbial decomposition. Here, we aim to use existing methods for quantifying OC stocks at the Arctic scale to (i) integrate new data for the total OC stock estimation and (ii) provide a first assessment of the OC storage in the form of organometallic complexes at the Arctic scale. This method partitions OC in three pools, each of which is assessed using different methods: the surface (0-3m) pool using a database coupled with soil taxonomy, the Yedoma domain (deep ice-rich sediment deposit) through a bootstrapping method and the Deltaic alluviums (thick river sediments) whose stock of complexed OC is assessed from existing total OC data and general trends. Our calculations led to a count of 360 GT of OC stabilized in the form of complexes across the Arctic, subdivided into (i) 263 GT for the 0-3 m pool with peak concentrations in Canada and Eastern Russia, (ii) ~75 GT within the Yedoma sediments and (iii) ~ 25 GT in the deltaic alluviums. Overall, it implies that 20-25% of the total OC pool is stabilized within metals complexes in the Arctic, and that this carbon is not directly available for microbial decomposition. Further assessments are therefore required, particularly regarding the behaviour of these complexes over time and under changing physicochemical conditions induced by climate change. Our research highlights the crucial need to incorporate mineral-OC interactions into climate models to enhance the accuracy of future permafrost carbon emission predictions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".