Photoacoustic detection and monitoring of oil spill
Bibliographic record
Abstract
The detection and monitoring of oil spills in marine environment are crucial to respond rapidly and efficiently and it is especially important in ice-covered areas. Detection and quantitative characterization of the affected areas as well as monitoring of remediation measures are critical for optimized cleaning operation and minimal environment impact. In the recent years, techniques of oil spill detection from under the ice with Remote Operated Vehicle (ROV) or Autonomous Underwater Vehicle (AUV), have been explored and have shown promising results. These techniques are based on ultrasonic or sonar technologies to quantify the oil volume and on optical techniques to obtain a chemical signature of the oil presence. In this paper we present a new promising technique based on photoacoustics for detection and sizing of oil spill under the ice, encapsulated within ice, or on open water. The technique has the advantage to provide signal in the presence of oil and no signal in its absence. It is also much less sensitive to alignment compared to ultrasonic and sonar techniques. Experimental results on detection of oil under the ice are presented and discussed. A first prototype with a scanning unit that can be operated in ROV is also presented. The solution proposed should be especially useful as a tool for emergency response, but should also be suitable when operated in AUV for monitoring high risky areas due to navigation, transportation or oil exploration and production.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".