A land‐to‐ocean perspective on the magnitude, source and implication of DIC flux from major Arctic rivers to the Arctic Ocean
Bibliographic record
Abstract
A series of seasonally distributed measurements from the six largest Arctic rivers (the Ob', Yenisey, Lena, Kolyma, Yukon and Mackenzie) was used to examine the magnitude and significance of Arctic riverine DIC flux to larger scale C dynamics within the Arctic system. DIC concentration showed considerable, and synchronous, seasonal variation across these six large Arctic rivers, which have an estimated combined annual DIC flux of 30 Tg C yr−1. By examining the relationship between DIC flux and landscape variables known to regulate riverine DIC, we extrapolate to a DIC flux of 57 ± 9.9 Tg C yr−1for the full pan‐arctic basin, and show that DIC export increases with runoff, the extent of carbonate rocks and glacial coverage, but decreases with permafrost extent. This pan‐arctic riverine DIC estimate represents 13–15% of the total global DIC flux. The annual flux of selected ions (HCO3−, Na+, Ca2+, Mg2+, Sr2+, and Cl−) from the six largest Arctic rivers confirms that chemical weathering is dominated by inputs from carbonate rocks in the North American watersheds, but points to a more important role for silicate rocks in Siberian watersheds. In the coastal ocean, river water‐induced decreases in aragonite saturation (i.e., an ocean acidification effect) appears to be much more pronounced in Siberia than in the North American Arctic, and stronger in the winter and spring than in the late summer. Accounting for seasonal variation in the flux of DIC and other major ions gives a much clearer understanding of the importance of riverine DIC within the broader pan‐arctic C cycle.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".