Microbial community dynamics following riverbank erosion across permafrost floodplains in the Yukon River basin
Bibliographic record
Abstract
Much of the organic-rich permafrost deposits in the Arctic lie in riverine floodplains where thaw due to polar amplification of climate change has accelerated bank erosion, leaving permafrost carbon deposits vulnerable to degradation by microorganisms. As sediments harbored in the riverbank erode, they are subjected to sediment transport processes, interact with the water column, and are eventually re-deposited in barforms on an opposing riverbank and incorporated to build new land with ensuing forest succession. Using a space-for-time substitution across these deposits and a suite of culture-independent amplicon and shotgun metagenomic sequencing of samples with ages from modern to several thousand years old collected from the Yukon River and its major tributary the Koyukuk River, we set out to understand microbial community succession associated with this process, and connect this with rates of carbon cycling in the subsurface. Because dioxygen is a special molecule concerning the fate of organic carbon in soils and sediments, we also developed a useful ’sequencing-as-sensing’ approach that leverages recent developments in protein language models to assess the time-integrated fraction of the microbial community capable of aerobic biology and oxidative attack of extracellular organic matter. Results revealed that permafrost deposits operate as a ‘seed bank’ that generates a pattern of succession toward an aerobic community capable of rapid carbon degradation during erosion and transport—a pattern that may help explain why carbon burial in river floodplains is so efficacious.
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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 | 0.001 | 0.001 |
| 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".