Bibliographic record
Abstract
Over the past decade, underwater gliders have been developed as a new autonomous sampling platform. These gliders are a type of autonomous underwater vehicle (AUV) that can be deployed in the ocean for weeks to months to collect in situ measurements in the world's oceans. Gliders allow us to complement traditional ship sampling by providing continuous spatial data, as opposed to ship-based casts which may be separated in the horizontal by tens to hundreds of kilometers. Oceanographic data is limited, however, by the instrument providing it. -- There are several different types of underwater gliders; the glider used in this research is the Slocum battery-powered glider produced by Webb Research. At a length of 1.5 m and a mass of 52 kg, these vehicles are easily deployed by just two people and make the process of collecting in-situ data quick and cost-effective. By default, the Slocum glider comes with a non-pumped Conductivity-Temperature-Depth (CTD) sensor; our research group has also installed an Aanderra Dissolved Oxygen Optode sensor, with future plans of incorporating different types of sensors to extend the platforms usability. An in-depth examination of the science sensors on board the glider must be performed in order to understand the limitations of the data collected. -- Here, we examine the data collected on the Newfoundland Shelf along with a study of the different sensor dynamics problems discovered during our research and field deployments. There is a well documented history of sensor dynamics issues in operational oceanography to which the Slocum glider is not immune. This work focuses on determining the specific sensor responses of the individual instruments on board the glider, and developing post-processing algorithms for the collected data to ensure all instruments sample at the same time interval. Algorithms developed are verified by testing against other independent sensors and appear to correctly minimize sensor response issues. Also, an analysis of how our local environment (strong winds) affect the operation of our Slocum at the surface is carried out, with an emphasis on the heading data from the Attitude sensor, and GPS location. The Slocum does align with the wind, similar to a weathervane, but wind effects are negligible.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".