Towards a Docking and Centering Mechanism for Underwater Welding Robots
Bibliographic record
Abstract
Underwater welding is currently one of the most dangerous jobs in North America. In the USA, commercial diving has an estimated fatality rate of 18.1 deaths per 10,000 divers per year [1]. Underwater welding requires high-voltage electrical systems to be operated in dynamic bodies of water, which puts welders at risk of electrocution if they get swept between the welding torch and the welding surface [2]. Drowning is also a common cause of death if welders get stuck in the bottleneck of differential pressures [2]. Despite its dangers, wet welding is still widely done by divers due to the requirement for a high degree of precision which is unachievable with current robots such as remotely operated vehicles (ROVs) [3]. The precision is impeded in part by the ROV’s poor ability to counter fluid-dynamic disturbances which affects the stability needed for welding. The fluid-dynamic disturbances cause trajectory calculation errors when centering the ROV around components that need welding. Because of these challenges, the high cost and complexity of robotic systems exceed the benefits. However, significant research is being done to improve ROV technology for construction [4], [5], which may result in more ROVs performing welding tasks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".