Cold-Temperate Mountainous Freshwater Produces Methane by Algal Metabolism
Bibliographic record
Abstract
We reported important environmental drivers of dissolved CH 4 concentrations (d-CH 4 ) in nutrient-limited mountainous freshwater in a cold-temperate region and explored the potential for multiple known oxic CH 4 production pathways. Field investigation revealed consistent supersaturated d-CH 4 in surface water (relative to the theoretical value of d-CH 4 at atmospheric equilibrium), with significant seasonal variations. Statistical analysis highlighted the direct impact of algal dynamics and the indirect effect of temperature and nutrients on d-CH 4 . Further lab-scale incubation demonstrated that CH 4 production decreased by 55.25 to 93.65% with algae removal, while it increased 4 to 10 times with methylphosphonate (MPn) amendment. These findings argued that CH 4 produced from algal metabolism related to MPn had a high potential for supersaturated d-CH 4 . It also verified the pivotal role of cyanobacteria in this mechanism, with temperature and light acting as regulatory factors. Through highlighting the role of algae for CH 4 characteristics in cold-temperate mountainous freshwater and proposing the potential of oxic CH 4 production through MPn metabolism in nutrient-limited lakes, this study enriches comprehension of aquatic CH 4 cycle and warns about the importance of preserving environmental balance in freshwater with minimal human disturbance.
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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 | 0.000 | 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".