Labrador Sea winter heat and freshwater content observations from glider and Argo data
Bibliographic record
Abstract
The Labrador Sea undergoes deep mixing in the wintertime, with mixed layer depths frequently reaching down to 2000 m. The resulting water mass that is formed - Labrador Sea Water (LSW) - has long been thought to be important for the deep Western Boundary Current (dWBC) and the upper limb of the AMOC. Direct observations of the overturning have, however, been rather limited. Limited Argo profiles and moorings in key locations offered winter measurements in a region challenged by severe weather conditions. Here we discuss observations of a winter-spring glider deployment in the Labrador Sea, but more specifically where deep convection occurs, from December 2019- to June 2020. Using the glider data, we describe the evolution of the mixed layer, changes in heat and freshwater content for surface (0-500 m) and intermediate depth (500-1000 m) layers for the central Labrador Sea convection region inside a box 200 by 100 km wide and spatial scales of T and S. We compare the observations with reanalysis data (air-sea heat fluxes and winds) and Argo profiles to better understand the variability missed by existing datasets. These observations highlight the role played by eddies in the overall variability of heat and salt in this region, something that is missed by Argo observations. They also show changes in spatial scales of T-S over the months from January to May, pointing towards the modulating effect of eddies on LSW winter formation.
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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".