Analysis of Underwater Target Detection Probability by Using Autonomous Underwater Vehicles
Bibliographic record
Abstract
Due to the trend of under-ocean exploration, realtime monitoring or long-term surveillance of the under-ocean environment, e.g., real-time monitoring for under-ocean oil drilling, is imperative. Underwater wireless sensor networks could provide an optimal option, and have recently attracted intensive attention from researchers. Nevertheless, terrestrial wireless sensor networks (WSNs) have been well investigated and solved by many approaches that rely on the electromagnetic/optical transmission techniques. Deploying an applicable underwater wireless sensor network is still a big challenge. Due to critical conditions of the underwater environment (e.g., high pressure, high salinity, limited energy etc), the cost of the underwater sensor is significant. The dense sensor deployment is not applicable in the underwater condition. Therefore, Autonomous Underwater Vehicle (AUV) becomes an alternative option for implementing underwater surveillance and target detection. In this article, we present a framework to theoretically analyze the target detection probability in the underwater environment by using AUVs. The experimental results further verify our theoretical results.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".