Quantification of Carbon Dioxide Gas Transfer Velocity by Scaling from Argon through Dual Tracer Gas Additions in Mountain Streams
Bibliographic record
Abstract
Quantification of the rate of gas exchange across the air-water interface is essential in understanding the biogeochemical cycling of carbon in mountain streams. However, estimating the gas transfer velocity (k) is not trivial, due to high turbulence and subsequent bubble-mediated gas transfer. Schmidt scaling is often used to estimate gas transfer velocities of climate relevant gases (e.g. CO2) from tracer gases (e.g. argon (Ar)), but this method has high uncertainty when scaling between gases of different solubilities in streams with bubble-mediated gas transfer. Here we explore a method for the estimation of gas exchange of CO2 from Ar by performing dual tracer gas additions in mountain streams. Ar and CO2 gas were simultaneously and continuously injected into streams and gas exchange rates were estimated using an exponential decline model. The mean ratio of gas exchange of Ar to CO2 (a) was 1.7 (95% credible interval of 1.3 to 2.3), approximately equal to the theoretical value of 1.7 (based both on Schmidt scaling and solubility). This result indicates that Ar can be used to estimate gas transfer of CO2 with scaling but with some uncertainty. Finally, modeled results suggest that the use CO2 as a tracer gas to measure gas exchange in streams with environmental conditions favoring interconversion to bicarbonate (i.e, high pH and alkalinity), can result in an overestimation of the gas transfer velocity k.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".