Variability of North Atlantic Water Mass Properties along Western Boundary and Interior Pathways
Bibliographic record
Abstract
The Atlantic Meridional Overturning Circulation (AMOC) exports cold, fresh, dense waters formed in the subpolar North Atlantic to equatorward latitudes along the western boundary and interior pathways. The properties of the water formed in the North Atlantic vary from year to year, however the strength and time scale for the downstream communication of this variability is still unclear. While several past studies have focused on tracking specific property anomalies, particularly from the Labrador Sea, we approach our study by investigating property variance downstream of the water mass source region. In effect, we aim to understand the downstream memory of water mass property variability in the North Atlantic along western boundary and interior pathways. To do so, we analyze hydrographic properties on neutral density isopycnal surfaces in the subpolar North Atlantic and along the western boundary and interior pathways with two reanalysis products from the Met Office, the hydrographic dataset (EN4) and ensemble prediction system (GloSea5), over their overlapping time period (1993-2019). Our results show different patterns of downstream variance for the interior compared to the western boundary, which we interpret in terms of known circulation features in the deep North Atlantic and what we have learned from past Lagrangian studies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".