Colored organic matter increases CO<sub>2</sub> in meso‐eutrophic lake water through altered light climate and acidity
Bibliographic record
Abstract
Abstract Many surface waters across the boreal region are browning due to increased concentrations of colored allochthonous dissolved organic carbon (DOC). Browning may stimulate heterotrophic metabolism, may have a shading effect constraining primary production, and may acidify the water leading to decreased pH with a subsequent shift in the carbonate system. All these effects are expected to result in increased lake water carbon dioxide (CO 2 ) concentrations. We tested here these expectations by assessing the effects of both altered allochthonous DOC input and light conditions through shading on lake water CO 2 concentrations. We used two mesocosm experiments with water from the meso‐eutrophic Lake Erken, Sweden, to determine the relative importance of bacterial activities, primary production, and shifts in the carbonate system on CO 2 concentrations. We found that DOC addition and shading resulted in a significant increase in partial pressure of CO 2 ( p CO 2 ) in all mesocosms. Surprisingly, there was no relationship between bacterial activities and p CO 2 . Instead the experimental reduction of light by DOC and/or shading decreased the photosynthesis to respiration ratio leading to increased p CO 2 . Another driving force behind the observed pC O 2 increase was a significant decrease in pH, caused by a decline in photosynthesis and the input of acidic DOC. Considering that colored allochthonous DOC may increase in a warmer and wetter climate, our results could also apply for whole lake ecosystems and p CO 2 may increase in many lakes through a reduction in the rate of photosynthesis and decreased pH.
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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 | 0.000 | 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".