Lateral and air-water inorganic carbon and methane fluxes in a small Arctic river: Seasonal variations and the connections with local hydrology
Bibliographic record
Abstract
The importance of rivers to the global carbon cycle is well recognized, and the need for more measurements from historically understudied systems including Arctic rivers is essential in reducing bias. The proximity of Freshwater Creek to the community of Iqaluktuuttiaq (Cambridge Bay), Nunavut, Canada, makes it an excellent site for time-series study that appreciates pronounced seasonality. This detailed investigation acquired data representative of the river's annual cycle, including the dynamic spring melt season. Freshwater Creek exported 3.12 Gg C y −1 as DIC and 848 kg C y −1 as dissolved CH 4 to the downstream coastal ocean. The river was a net emitter of CO 2 that released (net) 22.0 Mg C y −1 to the atmosphere, but also experienced periods of CO 2 uptake. CH 4 emissions were 450 kg C yr −1 , and all fluxes exhibited strong seasonality with maximum concentrations and fluxes during breakup. The significance of breakup was evident; half of the annual DIC and CH 4 exported to the ocean occurred within 21–31 and 8–13 days of the onset on breakup respectively. DIC/CO 2 and CH 4 fluxes in Freshwater Creek are connected to local hydrology, thus changes to the water cycle with climate change will influence the timing and magnitude of carbon fluxes in this system. Our detailed account of seasonal variability will be a useful framework for similar systems that cannot be monitored all year, will provide context for the interpretation of existing data, and will aid in developing sampling plans with intention to observe a river's full range of conditions. • DIC, CO 2 , and CH 4 fluxes have strong seasonality connected to local hydrology. • Peak DIC, CO 2 , and CH 4 concentrations and fluxes occur during spring breakup. • Half of the annual DIC and CH 4 export to ocean occurs within several weeks. • Annual DIC export to ocean is two orders of magnitude larger than net CO 2 emissions. • Annual CH 4 emissions are almost double the annual CH 4 export to the ocean.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".