Human Factors Issues with Operating Unmanned Underwater Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
There has been a great deal of human factors research on unmanned air and ground vehicles, but there is very little research examining the unique human factors problems associated with unmanned underwater vehicles (UUVs). The lack of research is surprising considering the increased use and the envisioned future use of UUVs in military maritime operations. In this paper, it is argued that because the underwater environment is so harsh and challenging, operating UUVs presents human factors problems that are different from the challenges of surface unmanned systems. Several common human factors problems are discussed when using unmanned systems, including the loss of sensory cues and spatial awareness, the control of the remote vehicle, problems with situation awareness and workload, and problems with trust in automation. In each case, these issues are discussed with respect to underwater operations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it