The Mixed Layer Salinity Balance in the Western Arctic Ocean
Bibliographic record
Abstract
Abstract In this study, we explore the mixed layer salinity (MLS) balance in the western Arctic Ocean based on the Arctic Subpolar gyre sTate Estimate (ASTE) results. The key components of the MLS budgets and their variabilities in response to the Beaufort Gyre (BG) spin‐up are identified. Seasonally, the surface forcing (brine rejection plus freshwater input) is the most important dominant contributor to the MLS balance. On the other hand, the entrainment dominates the interannual variability of MLS tendency inside the BG, while the advection dominates that in the Beaufort Sea. The sensitivity test of increased river discharge revealed a greater role of the advection term, along with weakened contributions from the surface forcing and entrainment, in determining the interannual variability of MLS balance. In contrast, the seasonal variabilities remained largely unchanged. The Lagrangian particle tracking reveals that the majority of BG freshwater within the mixed layer exits through the Canadian Archipelago prior to the BG spin‐up (2002–2006) and during its relaxation (2012–2017). We found a reduction in mixed layer freshwater sources from the external BG that could feed the gyre during its spin‐up (2007–2011), with the major contributions coming from the Beaufort Sea and the BG region itself through Ekman convergence. The mixed layer freshwater pathways are similar in the two versions of ASTE, but with noticeable proportion changes with the increasing river discharge.
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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) | 0.000 | 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".