Hardware design and implementation for underwater surface integration
Bibliographic record
Abstract
Due to the poor visibility in most underwater environments, it is difficult to obtain a clear, large-scale image of the scene being surveyed. For this reason, a system has been developed to capture many close-up images from different locations in the test site, and integrate these into a composite planar or 3D surface. The system can be used for inspection of boat hulls and underwater structures to provide superior information more economically and safely than standard techniques. The system utilizes an underwater remotely operated vehicle (ROV) controlled by a user which allows a rapid inspection process without the need for human divers to enter the water. The ROV has a sonar positioning system and gyroscope-based orientation sensing hardware. Each image of the underwater scene is saved along with the video camera's instantaneous position and orientation. The images are then patched together into a large composite picture of the structure which can be viewed from different locations using computer graphics. This system has been tested and shown as a practical and potentially very useful underwater inspection tool.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".