Contrasting CO<sub>2</sub> concentration discharge dynamics in headwater streams: A multi‐catchment comparison
Bibliographic record
Abstract
Abstract Aquatic CO2 concentrations are highly variable and strongly linked to discharge, but until recently, measurements have been largely restricted to low‐frequency manual sampling. Using new in situ CO2 sensors, we present concurrent, high‐frequency (<30 min resolution) CO2 concentration and discharge data collected from five catchments across Canada, UK, and Fennoscandinavia to explore concentration‐discharge dynamics; we also consider the relative importance of high flows to lateral aquatic CO2 export. The catchments encompassed a wide range of mean CO2 concentrations (0.73–3.05 mg C L−1) and hydrological flow regimes from flashy peatland streams to muted outflows within a Finnish lake system. In three of the catchments, CO2 concentrations displayed clear bimodal distributions indicating distinct CO2 sources. Concentration‐discharge relationships were not consistent across sites with three of the catchments displaying a negative relationship and two catchments displaying a positive relationship. When individual high flow events were considered, we found a strong correlation between both the average magnitude of the hydrological and CO2 response peaks, and the average response lag times. An analysis of lateral CO2 export showed that in three of the catchments, the top 30% of flow (i.e., flow that was exceeded only 30% of the time) had the greatest influence on total annual load. This indicates that an increase in precipitation extremes (greater high‐flow contributions) may have a greater influence on the flushing of CO2 from soils to surface waters than a long‐term increase in mean annual precipitation, assuming source limitation does not occur.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".