Bibliographic record
Abstract
Dissolved organic matter (DOM) leaching from thawing permafrost may promote a retroaction loop onto climate if it is efficiently mineralized into greenhouse gases. Yet, many uncertainties remain on the extent of this mineralization, which depends on DOM lability that is seemingly quite variable across landscapes. Thermokarst peatlands are organic-rich systems where some of the largest greenhouse gas (GHG) emission rates have been measured. At spring turnover, anoxic waters are releasing the GHG accumulated in winter, and the DOM pool is being exposed to sunlight. Here, we present an experiment where DOM photo- and bioreactivity were investigated in water collected from a thermokarst lake in a subarctic peatland during late winter (after six months of darkness). We applied treatments with or without light exposure, and manipulated the bacterial abundance with the aim to quantify the unique and combined effects of light and bacteria on DOM reactivity at ice-off in spring. We demonstrate that sunlight was clearly driving the transformation of the DOM pool, partly leading to its full mineralization into CO2. About 18 % of initial dissolved organic carbon (DOC, a loss of 3.9 mgC L−1) was directly lost over 18 days of sunlight exposure in a treatment where bacterial abundance was initially reduced by 95 %, while dark incubations lead to very limited changes in DOC, regardless of the bacterial abundance and activity. The highest DOC loss was observed for the treatment with the full bacterial community exposed to sunlight (5.0 mgC L−1), indicating an undirect effect of light through the bacterial consumption of photoproducts. Sunlight was an outstanding boosting factor on bacterial growth when grazers were eliminated, leading to the recovery of the original bacterial abundance in about 8 days. The results also show that only half of the light-associated DOC losses were converted into CO2, with the rest potentially turned into particles through photo-flocculation. Sunlight should therefore play a major role in DOM processing, CO2 production and carbon burial in peatland lakes during spring, likely lasting for the rest of the open-season in mixing surface layers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.022 | 0.014 |
| Insufficient payload (model declined to judge) | 0.056 | 0.065 |
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".