Calcium and amorphous silica in Arctic soils: Estimating Pan-Arctic availabilities and importance for CO2 production
Bibliographic record
Abstract
Future warming of the Arctic not only threatens to destabilize the enormous pool of organic carbon accumulated in permafrost soils, but may also mobilize elements such as calcium (Ca) or silicon (Si). Little is known about the effects of Si and Ca on carbon cycle processes in soils from Siberia, the Canadian Shield or Alaska. We incubated five different soils for six months with different Ca and amorphous Si (ASi) concentrations. Our results show a strong decrease in soil CO2 production for all soils with increasing Ca concentrations. The ASi effect was not clear across the different soils used, with soil CO2 production increasing, decreasing or not being significantly affected depending on the soil type and if the soils were initially drained or waterlogged. Including Ca as a controlling factor for Arctic soil CO2 production rates may therefore reduce uncertainties in modelling future scenarios on how Arctic regions may respond to climate change. To project how biogeochemical cycling in Arctic ecosystems will be affected by climate change, there is a need for data on element availability. For this we analysed ASi, Si, Ca, iron (Fe), phosphorus (P), and aluminium (Al) availability from 574 soil samples from the circumpolar Arctic region. We show large differences in element availability among different lithologies and Arctic regions. We summarized these data in pan-Arctic element maps focussing on the top 100 cm of Arctic soil. Furthermore, we provide values for element availability for the organic and the mineral layer of the seasonally thawing active layer as well as for the uppermost permafrost layer. Our spatially explicit data on differences in the availability of elements between the different lithological classes and regions now and in the future will improve Arctic Earth system models for estimating current and future carbon and nutrient feedbacks under climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".