Eddy Seeding in the Labrador Sea: A Submerged Autonomous Launch Platform Application
Bibliographic record
Abstract
Abstract Two submerged autonomous launch platforms (SALPs) were deployed at 500-m depth on a deep-water mooring in the northeastern Labrador Sea from 2007 to 2009 to automatically release profiling floats into passing warm-core anticyclonic Irminger Rings (IRs). The objective was to investigate the rings’ vertical structure and evolution as they drifted from their formation site near the western coast of Greenland to the area of deep convection in the south-central part of the basin. Mechanically and electronically, the SALP worked well: 10 out of 11 floats were successfully released from the mooring over 2 years. However, getting floats trapped in eddy cores using a preprogrammed release algorithm based on temperature and pressure (a proxy for current speed) measured by the SALPs met with limited success mainly because 1) the floats settled at a park pressure that was initially too deep, below the volume of water trapped in the eddy core; 2) the eddies translated past the mooring much more quickly than anticipated; and 3) there is a seasonal cycle in both background and eddy core temperature that was not known a priori and therefore not accounted for in the release algorithm. The other mooring instruments (100–3000 m) revealed that 12 anticyclones passed by the mooring in the 2-yr monitoring period. Using this independent information, the authors assessed and improved the release algorithm, still based on ocean conditions measured at one depth, and found that much better performance could have been achieved with an algorithm that allowed for faster eddy translation rates and the seasonal temperature cycle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".