Bioreactivity of dissolved organic carbon in ponds of the ice-wedge polygonal tundra
Bibliographic record
Abstract
Abstract. The role of ponds in transforming laterally exported dissolved organic matter (DOM) within polygonal landscapes affected by degrading ice-wedges remains poorly understood, despite their potential importance in carbon cycling. We hypothesized that the morphological and limnological diversity of ponds–driven by permafrost erosion and soil subsidence–generates DOM of varying bioreactivity. To test this, we conducted a 188-day bioassay using water from 15 ponds representing the main geomorphological pond types in a polygonal landscape in northeastern Canada. Using optical spectroscopy, we examined the relationship between DOM properties and its bioreactivity. We also conducted a parallel bioassay experiment with nutrient additions to assess potential inorganic nitrogen and phosphorus limitations. Results show that a significant proportion of dissolved organic carbon (DOC) is available to bacterioplankton in these shallow lentic systems during summer (33 % decomposed after 97 days). Contrary to our hypothesis, and despite variations in DOM composition, no difference in DOC loss was observed among the three pond categories defined in this study, suggesting comparable bioavailable DOC pools. Moreover, nutrient addition did not significatively enhance DOC loss or decay rates, suggesting that bacterial decomposition depends mainly on organic matter bioavailability. This is further supported by a positive correlation between DOC loss and tryptophan-like fluorophores, a marker of bioavailable DOM. This suggests that DOM released by cyanobacterial mats and other autochthonous producers may be more readily utilized by bacteria than DOM derived from peaty soils. These findings highlight the importance of freshly produced organic matter in regulating carbon cycling in ponds of the ice-wedge polygonal tundra, with consequences on the fate of carbon released from thawing 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 | 0.000 | 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) | 0.000 | 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".