The development of AUV strategies for multidisciplinary use
Bibliographic record
Abstract
The Marine Environmental Research Lab for Intelligent Vehicles (MERLIN) operates an Explorer Autonomous Underwater Vehicle (AUV) that serves as a potential platform for ocean surveys over a broad range of disciplines. MERLIN undertook a four year development program to improve both the capacity and autonomy of the vehicle by acquiring multibeam, side-scan and sub-bottom profiling sonars and developing algorithms using these tools to improve the navigation of the vehicle as part of a project called Responsive AUV Localization and Mapping (REALM). The goal of REALM is to be able to pre-program the vehicle to both correct its own dead-reckoning position in a survey area and allow it to recognize Zones of Interest (ZOI). With such an improvement, programmed surveys could be modified in real time to allow the collection of additional detailed data in a ZOI. To develop an understanding of the types of data that might indicate a ZOI during a typical survey, a field program was developed for a site in Smith Sound, Newfoundland, Canada. Smith Sound was the site of the largest known overwintering inshore cod population in North America at a time when surrounding cod populations largely collapsed, creating interest in possible relationships between seabed properties and cod populations; the Sound is also of interest due to the potential presence of internal seiches and resonant tidal forcing; the area is the location of many documented, but unlocated shipwrecks; and, with depths over 200 m, is a region that is not easily explored without the use of technologies such as AUV's or other underwater vehicles. This paper presents the results of the preliminary Smith Sound survey, including an overview of the logistical planning aimed at collecting data with multidisciplinary interest. The individual data sets are presented, highlighting preliminary indications of ZOI for each topic of interest.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".