Diverging pond dissolved organic matter characteristics yield similar CO<sub>2</sub> flux potentials in a disturbed High Arctic landscape
Bibliographic record
Abstract
Abstract Climate warming and permafrost thaw have the potential to shift Arctic carbon (C) cycling dynamics so ponds, which represent over a quarter of northern circumpolar surface water area, may play a larger role in the mineralization of terrestrial C and emission of greenhouse gases (GHG). Here, we constrain how active layer detachments (ALDs) affect C cycling dynamics in High Arctic ponds (n = 19) through the examination of geochemistry, dissolved organic matter (DOM) characteristics, and dissolved GHG concentrations. Summer rainfall events were identified as the primary source of pond water over a 5 week period during the monitored thaw season. We observed two distinct geochemical and DOM composition groups in ponds surrounded by undisturbed, vegetated areas compared to ponds located within the geomorphologically-disturbed ALDs. DOM in undisturbed areas had characteristics suggesting allochthonous origin from modern vegetation. Ponds in the ALDs had lower mean dissolved organic carbon (DOC) concentrations than ponds within undisturbed landscapes, with DOM characterization suggesting greater proportions of autochthonous DOM. Observed differences in DOC concentrations and DOM composition between ponds located within the disturbed and undisturbed landscapes did not translate into significant differences in dissolved CO2 concentrations among pond types. We conclude that our observed changes in DOM composition and characteristics in High Arctic ponds may not result in substantial increases in GHG flux as a result of continued Arctic warming.
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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 | 0.000 | 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".