Emissions of carbon dioxide and methane from a headwater stream network of interior Alaska
Bibliographic record
Abstract
Boreal ecosystems store significant quantities of organic carbon (C) that may be vulnerable to degradation as a result of a warming climate. Despite their limited coverage on the landscape, streams play a significant role in the processing, gaseous emission, and downstream export of C, and small streams are thought to be particularly important because of their close connection with the surrounding landscape. However, ecosystem carbon studies do not commonly incorporate the role of the aquatic conduit. We measured carbon dioxide (CO2) and methane (CH4) concentrations and emissions in a headwater stream network of interior Alaska underlain by permafrost to assess the potential role of stream gas emissions in the regional carbon balance. First‐order streams exhibited the greatest variability in fluxes of CO2 and CH4, and the greatest mean pCO2. High‐resolution time series of stream pCO2 and discharge at two locations on one first‐order stream showed opposing pCO2 responses to storm events, indicating the importance of hydrologic flowpaths connecting CO2‐rich soils with surface waters. Repeated longitudinal surveys on the stream showed consistent areas of elevated pCO2 and pCH4, indicative of discrete hydrologic flowpaths delivering soil water and groundwater having varying chemistry. Up‐scaled basin estimates of stream gas emissions suggest that streams may contribute significantly to catchment‐wide CH4 emissions. Overall, our results indicate that while stream‐specific gas emission rates are disproportionately high relative to the terrestrial landscape, both stream surface area and catchment normalized emission rates were lower than those documented for the Yukon River Basin as a whole. This may be due to limitations of C sources and/or C transport to surface waters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".