The Autonomous Underwater Vehicle Integrated with the Unmanned Surface Vessel Mapping the Southern Ionian Sea. The Winning Technology Solution of the Shell Ocean Discovery XPRIZE
Bibliographic record
Abstract
The methods of data collection, processing, and assessment of the quality of the results of a survey conducted at the Southern Ionian Sea off the Messinian Peninsula, Greece are presented. Data were collected by the GEBCO-Nippon Foundation Alumni Team, competing in the Shell Ocean Discovery XPRIZE, during the Final Round of the competition. Data acquisition was conducted by the means of unmanned vehicles only. The mapping system was composed of a single deep water AUV (Autonomous Underwater Vehicle), equipped with a high-resolution synthetic aperture sonar HISAS 1032 and multibeam echosounder EM 2040, partnered with a USV (Unmanned Surface Vessel). The USV provided positioning data as well as mapping the seafloor from the surface, using a hull-mounted multibeam echosounder EM 304. Bathymetry and imagery data were collected for 24 h and then processed for 48 h, with the extensive use of cloud technology and automatic data processing. Finally, all datasets were combined to generate a 5-m resolution bathymetric surface, as an example of the deep-water mapping capabilities of the unmanned vehicles’ cooperation and their sensors’ integration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".