Thermal vs. Haline Drivers of Springtime Restratification in the Labrador Sea
Bibliographic record
Abstract
[1]¿p#1 newcommands The Labrador Sea is a vital component of global ocean circulation, hosting vigorous deep convection that can mix to deeper than 2000 m. Features that drive convection are widely studied, but a lack of high-resolution, in-situ observations hinder our understanding of how small scale features influence the convective process and trigger restratification in spring, particularly at the submesoscales (< 10 km). This study assesses contributions of temperature and salinity to both vertical and horizontal stratification during early winter, convection, and spring restratification, using data collected by five underwater gliders deployed during winter 2019/20 and 2021/22. Using the Turner angle, we show that vertical stratification in the Labrador Sea shifts from salinity-stratified in early winter to temperature-unstable during convection, and back to salinity-stratified in the early restratification period. Horizontally, both warm and fresh intrusions drive lateral density anomalies during restratification, with the haline influence dominating. Wavelet analysis of these anomalies illustrates increased submesoscale activity during restratification, and comparisons of thermohaline contributions show the predominance of salinity-driven submesoscale fronts. These fronts have the potential to rapidly stratify the water column, and illustrate the importance of freshwater intrusions at submesoscales in halting convection.
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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 | 0.000 | 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".