Progress in predicting the performance of ocean gliders from at-sea measurements
Bibliographic record
Abstract
With over 100 commercially-available ocean gliders being used by researchers around the world, there is strong evidence that these platforms have become the tool of choice for those who require continuous sampling of ocean properties over a range of user-controllable depths. Researchers continue to add new sensors to these vehicles usually on the external surfaces where a sensor can work in an essentially unobstructed flow condition. These added sensors change the behaviour of the glider. For the purpose of improving our predictions of the behaviour of a glider during steady-state glides and course-changing manoeuvres, it is useful to have a simple analytical hydrodynamic model which can be validated quickly using at-sea measurements during several descending and ascending glides. The purpose of this paper is twofold: (i) to show how the hydrodynamic properties which govern steady-state gliding can be extracted from measurements made with on-board sensors, and, (ii) to show how these hydrodynamic properties can be used to predict the performance of ocean gliders (e.g. glide angle, glide speed, duration of voyage etc.). We describe a three-parameter model which has proved useful in representing the behaviour of an ocean glider during straight-line descents and ascents. This parametric model has been validated with at-sea measurements during multiple glides. Estimates for these parameters can be obtained from the measurements of four quantities on-board a Slocum ElectricTM glider, namely (i) the fore-and-aft position of the pitch-control battery, (ii) the volume of seawater which is ingested or expelled by the buoyancy engine, (iii) the glider pitch angle, and, (iv) the glider depth. We describe briefly a method for obtaining estimates for three of these parameters and show some results in terms of the glider drag and lift coefficients over a wide range of operating conditions. Additional work is outlined to obtain estimates for the parameters which determine the pitching moment behaviour of this ocean glider.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".