Weak mineralization despite strong processing of dissolved organic matter in Eastern Arctic tundra ponds
Bibliographic record
Abstract
Abstract Permafrost thawing mobilizes large quantities of organic carbon that was sequestered in Arctic regions over the last glacial cycle. Processes involved in the oxidation of this carbon need to be further assessed to estimate the fraction to be released into the atmosphere. Shallow tundra ponds are sites of active carbon turnover on the landscape and significant sources of greenhouse gases. Dissolved organic matter (DOM) leached from thawing peat into these ponds is exposed to sunlight, with the potential to accelerate its mineralization directly into CO2 or through the production of more labile molecules. We tested the catalytic effect of sunlight on DOM mineralization in tundra ponds formed on organic‐rich polygonal landscapes originating from syngenetic permafrost, including a pond exposed to active permafrost erosion. Microbial decay rates, measured as the loss of chromophoric DOM, were similar to photodecay rates (1%–3% d−1). Groups of fluorescing molecules were formed through microbial transformation or lost through photolysis at differing rates among studied ponds, with the erosive trough pond presenting a unique response suggesting the involvement of soil microbes. Despite the stimulation of microbial growth under sunlight and the dynamic response of DOM optical properties, the loss of dissolved organic carbon was not significant under any treatment. This suggests that microbial and photochemical mineralization of DOM was slow and potentially substrate‐limited during the dry period when ponds were sampled. The static nature of tundra ponds, with their long water retention time, may thus constrain hot moments when water moves and transports carbon on the landscape.
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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) | 0.000 | 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".