Waterfalls enhance regional methane emissions by enabling dissolved methane to bypass microbial oxidation
Bibliographic record
Abstract
River waters are significant sources of atmospheric methane whose local emissions increase with river slope and turbulence. However, when integrated regionally, the amount of dissolved methane released to the atmosphere is uninfluenced by local changes in turbulence when no additional loss mechanisms are present. Here we tested the hypothesis that waterfalls enhance both local and regional atmospheric methane emissions if microbial methane oxidation is significant in river waters. Rates of net atmospheric emission and net aerobic methane oxidation were measured in river waters containing waterfalls across western New York revealing that methane oxidation can diminish atmospheric emissions when turbulence is less. However, at waterfalls, 88 ± 1% of the dissolved methane supersaturation was released to the atmosphere, increasing net methane emission rates substantially beyond oxidation (0.1–16.2 × 106 nM d-1 for waterfall emission; 10–39 nM d-1 for oxidation), and ultimately enhancing regional methane emissions by enabling dissolved methane to bypass an oxidative sink. Waterfalls can substantially increase methane emissions from rivers with high microbial oxidation rates, because turbulence allows methane to bypass oxidation, according to in-situ measurements and water sampling of rivers in New York State
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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".