Gas Transfer Velocities Evaluated Using Carbon Dioxide as a Tracer Show High Streamflow to Be a Major Driver of Total CO<sub>2</sub> Evasion Flux for a Headwater Stream
Bibliographic record
Abstract
Abstract Evasion of carbon dioxide (CO2) from headwater streams is a dominant process controlling the fate of terrestrially derived carbon in inland waters. However, limitations of sampling techniques inhibit efforts to accurately characterize CO2 evasion from streams, and particularly headwater streams with steep gradients, complex morphologies, and challenging terrain. CO2 source dynamics coupled with turbulence conditions control gas transfer velocities of CO2 ( ) and therefore drive CO2 evasion. We present estimates of and CO2 evasion from a steep, turbulent headwater stream in southwestern British Columbia, Canada, collected using an automated in situ CO2 tracer technique. Gas transfer velocities scaled positively with discharge, with a median of 36.8 m/day and a range of 13.5 to 169 m/day. Gas transfer velocities were highest during high‐flow events, with 84% of all CO2 emissions occurring when discharge was higher than Q50, the median discharge (92.6 L/s). Widely used models overestimated gas transfer velocities with a mean relative error of 24% but underestimated k600 values above 165 m/day. Our determinations of gas transfer velocities for a range of streamflow suggest that CO2 evasion may be higher than previously estimated from direct measurements or models, particularly during high‐flow events. These findings illustrate the need for direct, frequent, in situ determinations of to accurately characterize CO2 evasion dynamics in steep headwater streams.
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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 | 0.001 | 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".