Does Diapirism Influence Greenhouse Gas Production on Patterned Ground in the High Arctic?
Bibliographic record
Abstract
There are unusual patterns of greenhouse gas (GHG) net production in soil profiles of Arctic polar deserts. These deserts include frost boils that are symptomatic of permafrost‐associated soils. Some frost boils contain diapirs, intrusions of recently thawed, carbon‐ and water‐rich fine material pushed upward into the overlying active layer. Here we identified diapir‐associated frost boils in an Arctic polar desert that we had previously found to have highly variable patterns of GHG net production, and compared patterns of GHG net production in soil profiles between diapir and non‐diapir frost boils. In addition, we tested the repeatability of soil gas probes measurements and if estimates of diffusivity based on bulk density were accurate. Probes were installed in frost boils identified as including or not including diapirs, and measurements were conducted over several days to evaluate net GHG production. Soil gas probes deployed for longer than approximately 3 d showed loss of signal, and the injection of an inert tracer, SF6, validated our estimates of soil diffusivity based on bulk density. Diapir‐associated frost boils showed reduced soil respiration compared with non‐diapir frost boils, despite these diapir‐associated frost boils having increased soil organic matter content. Thus, diapir intrusions in frost boils of the Arctic polar desert simultaneously store greater amounts of organic C and reduce soil respiration compared with non‐diapir frost boils. Differences in soil organic matter quality and/or its interaction with soil texture may be an important control for carbon storage in Arctic soils.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".