The Ephemeral Signature of Permafrost Carbon in an Arctic Fluvial Network
Bibliographic record
Abstract
Abstract Arctic fluvial networks process, outgas, and transport significant quantities of terrestrial organic carbon (C), particularly dissolved organic carbon (DOC). The proportion of permafrost C in these fluxes, however, is poorly constrained. A primary obstacle to the quantification of permafrost‐derived DOC is that it is rapidly respired without leaving a unique tracer of its presence. In this study, we investigated the production of bacterial respiratory carbon dioxide (CO2; measured as dissolved inorganic carbon; DIC) during maximum late‐summer thaw in sites spanning a fluvial network (Kolyma Basin, Siberia) to assess whether the biodegradation of permafrost DOC could be detected by the presence of a persistent aged (14C‐depleted) signature on the DIC pool. Using Keeling plot interpretation of DIC produced in bioincubations of river water, we show that bacteria respire varying sources of DOC moving downstream through the fluvial network. Respiration of permafrost (production of aged CO2) was only detected in heavily permafrost thaw influenced sites. In nonpermafrost thaw impacted sites, ambient DIC was modern (14C‐enriched), but rather than precluding the respiration of permafrost OC upstream, we suggest that 14C‐depleted DIC is overwhelmed by modern DIC. Investigation of dissolved organic matter composition via Fourier transform ion cyclotron resonance mass spectrometry highlighted that elevated levels of aliphatic and nitrogen‐containing compounds were associated with the production of aged DIC, providing molecular‐level insight as to why permafrost‐derived dissolved organic matter is rapidly respired. Overall, results from this study demonstrate the difficulty of tracing inputs of a highly reactive substrate to systems with diverse organic matter sources.
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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 | 0.000 | 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".