Lagrangian transport across the upper Arctic waters in the Canada Basin
Bibliographic record
Abstract
The goal of this paper is to study transport, from a Lagrangian perspective, across selected circulation patterns in the upper Arctic Ocean waters. To this end, we apply the methodology of Lagrangian descriptors, using the function M, to the velocity field dataset provided by the Copernicus Marine Environment Monitoring Service. We focus our analysis on the Arctic region in the halocline (top 30 m depth), which is based on particular events occurring over the 2012–2016 time period. The advantage of the Lagrangian descriptor is that it highlights large‐scale persistent dynamical structures relating to mathematical objects known as invariant manifolds, which determine fluid transport and mixing processes. These geometric flow structures play a crucial role in the evolution of the salinity content observed over the Arctic Basin. In this work, we identify these dynamical structures in the Beaufort Sea and show how they mediate transport processes according to a clockwise circulating pattern related to the Beaufort Gyre. Additionally, this approach highlights the importance of the Transpolar Drift Stream (TDS) as a transport barrier which maintains the salinity gradient between the Canada Basin and the Atlantic waters. Our approach also illustrates the variability of the intensity of the TDS during the analysed period and identifies secondary currents that feed it.
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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 | 0.001 |
| 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 | 0.000 |
| 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".