Diffusive and ebullitive transport of methane and nitrous oxide from streams: Are bubble-mediated fluxes important?
Bibliographic record
Abstract
[1] Using measured rates of bubble release and diffusive gas transport (also termed surface aeration), we address the role of these transport mechanisms in emissions of nitrous oxide and methane from four streams. While ebullition in streams and rivers has received little study, we found that ebullition was an important mode of methane emissions, contributing 20%–67% of methane emissions (among streams). Nitrous oxide emissions via ebullition were negligible (<0.1% of diffusive emissions). Total methane emissions (ebullition + diffusive transport) were over ten times greater than N2O emissions in terms of CO2 equivalents. Rates of bubble release were highly variable, ranging from 20 mL m−2 d−1 to 170 mL m−2 d−1 (seasonal average among streams, with volumes reflecting ambient temperature and pressure). Methane was the most abundant of the bubble gases that were measured (26% by volume on average among streams), followed by carbon dioxide (1% on average) then nitrous oxide. Average bubble nitrous oxide concentrations were below atmospheric mixing ratios for the majority of streams; however, one stream showed concentrations as high as 3600 ppbv. Sediment characteristics were strong predictors of bubble composition. Concentrations of methane and nitrous oxide were positively related to the proportion of fine sediments. High methane concentrations in bubbles were related to high sediment organic carbon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".