Bibliographic record
Abstract
Abstract. The Siberian Arctic Ocean (SAO) is the largest integrator and redistributor of Siberian freshwater resources and acts to significantly influence the Arctic climate system. Moreover, the SAO is experiencing some of the most notable climate changes in the Arctic, and advection of anomalous Atlantic- (atlantification) and Pacific-origin (pacification) inflow waters and biota continue to play a major role in reshaping the SAO in recent decades. However, logistical challenges have limited our observation-based understanding of the upper SAO. In this study, we use a large collection of mooring data to create a coherent picture of the spatiotemporal patterns and variability of currents and shear in the upper SAO during the past decade. Although there was no noticeable trend in the upper SAO's current speed and shear from 2013 to 2023, their seasonal cycle has significantly strengthened. The cycle follows a coherence of upper ocean dynamics and sea ice state, as evidenced by the high correlation (–0.94) between seasonal cycles of sea ice concentration and current shear (less ice drives stronger currents and shear). In the shallow (<20–30 m) summer surface mixed layer, currents have increased because strong stratification prevents wind energy from propagating into the deeper layers. In this case, strong near-inertial currents account for more than half of the summertime current speed and shear. In the winter, a thicker surface layer is created by deep upper SAO ventilation due to atlantification, which distributes wind energy to far deeper (>100 m) layers. These findings are critical to understanding the ramifications for mixing and halocline weakening, as well as the rate of atlantification in the region.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.008 |
| Insufficient payload (model declined to judge) | 0.312 | 0.175 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".