Towards navigation of underwater gliders in seasonal sea ice
Bibliographic record
Abstract
The suitability of the available navigational aids for underwater gliders for year round use in waters which experience seasonal sea ice is evaluated and a path towards an operational system on the Labrador Shelf is presented. The extent of ice coverage is generally found to be limited to the shelf areas and with a duration of up to 20 weeks. For a desired navigational accuracy of 100 meters over a potential trackline in from the shelf break and back out again, around 400 kilometers, a series of low frequency sound sources or geophysical navigational methods are proposed. Acoustic methods require more maintenance and are more prone to loss, while geophysical methods require additional evaluation in the operational region and potential digital elevation model refinement. A three phase strategy is proposed to enable under ice observations. The first phase involves operating the gliders in the ice free season over the proposed track-lines. This data collection phase would allow the evaluation of the available methods and build confidence for later under ice operations. The second phase involves the refinement of the available DEMs both bathymetric and magnetic to the degree that successful navigation by geophysical methods is achieved during the ice free season. Upon the success of the vehicles navigation without surface access during the ice free season, the third phase would commence, that of under ice observations.
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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 | 0.001 | 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".