Arctic Freshwater Sources and Ocean Mixing Relationships Revealed With Seawater Isotopic Tracing
Bibliographic record
Abstract
Abstract The Arctic Ocean and adjacent seas are undergoing increased freshwater influx due to enhanced glacial and sea ice melt, precipitation, and runoff. Accurate delineation of these freshwater sources is vital as they critically modulate ocean composition and circulation with widespread and varied impacts. Despite this, the delineation of freshwater sources using physical oceanographic measurements (e.g., temperature, salinity) alone is challenging and there is a requirement to improve the partitioning of ocean water masses and their mixing relationships. Here, we complement traditional oceanographic measurements with continuous surface seawater isotopic analysis (δ 18 O and deuterium excess) across a transect extending from coastal Alaska to Baffin Bay and the Labrador Sea conducted from the US Coast Guard Cutter Healy in Autumn 2021. We find that the diverse isotopic signatures of Arctic freshwater sources, coupled with the high freshwater proportion in these marine systems, facilitates detailed fingerprinting and partitioning. We observe the highest freshwater composition in the Beaufort Sea and Amundsen Gulf regions, with heightened freshwater content in eastern Baffin Bay adjacent to West Greenland. We apply isotopic analysis to delineate freshwater sources, revealing that in the Western Arctic freshwater inputs are dominated by meteoric water inputs—specifically the Mackenzie River—with a smaller sea ice meltwater component and in Baffin Bay the primary sources are local precipitation and glacial meltwater discharge. We demonstrate that such freshwater partitioning cannot be achieved using temperature‐salinity relationships alone, and highlight the potential of seawater isotopic tracers to assess the roles and importance of these evolving freshwater sources.
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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 | 0.000 | 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".