Underwater Remote Sensing of Oil: Validation Testing of a Commercial-Off-The-Shelf Acoustic Sensor for Emergency Response
Bibliographic record
Abstract
During an oil spill, emergency responders need to be able to estimate the thickness of oil to determine the type of recovery equipment to use and the environmental impact for natural resource damage assessment, perform a risk assessment, and generate response plans. There is an abundance of remote sensing techniques and devices that have been evaluated for use in oil spill emergency response. One that has repeatedly shown promise is active sound navigation and ranging (SONAR) system that transmits and receives acoustic signals. However, the systems that have been tested are often prototypes, not readily available during an oil spill response. This project evaluated the precision and accuracy of a commercial-off-the-shelf (COTS) acoustic sensing device, the AQUAscat 1000R by Aquatec Group (Basingstoke, UK) equipped with four high frequency transducers, to determine slick thickness during oil spill emergency response. To assess the AQUAscat’s capabilities in a range of different conditions, four phases of testing were conducted in controlled high bay, wave, ice, and outdoor tanks. Marine diesel (MD), Mississippi Canyon Block 20 (MC20) crude oil, and Canadian pipeline petroleum crude oil (rainbow light crude) were analyzed for density and sound speed and used during testing. The AQUAscat’s resolution (i.e., sensitivity) and lower detection limit were 1.25 mm and 2.5 mm, respectively. This thickness is above the typical actionable value for recovery of surface slicks (~0.2 to 1 mm). The AQUAscat would be most applicable for situations where there are thick slicks or pockets of oil, such as under ice, or for calibrating other devices. The COTS AQUAscat will not routinely be a useful tool for responders to assess floating slick thickness during spills when compared to other remote sensing devices (e.g., infrared and thermal cameras on uncrewed aerial platforms).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".