AIRBORNE OIL SPILL SENSOR TESTING: PROGRESS AND RECENT DEVELOPMENTS
Bibliographic record
Abstract
ABSTRACT It is now possible to measure the thickness of an oil slick on water by remote sensing. A laboratory sensor has been developed to provide this absolute oil slick thickness measurement. A joint project between Environment Canada, U.S. Minerals Management Service (MMS), Imperial Oil Research Ltd., and Industrial Materials Institute of the National Research Council of Canada has led to the development of a prototype slick thickness measurement system, known as the Laser Ultrasonic Remote Sensing of Oil Thickness (LURSOT) sensor. This prototype was the first step in achieving the ultimate goal of providing an airborne sensor for the remote measurement of oil slick thickness on water. The LURSOT sensor employs three lasers to produce and measure the time-of-flight of ultrasonic waves in oil, hence providing a direct measurement of oil slick thickness. The successful application of this technology to the measurement of oil slick thickness will benefit (1) the scientific community as a whole by providing information about the dynamics of oil slick spreading and (2) the spill responder by providing a measurement of the effectiveness of spill countermeasures such as dispersant application. The first part of this paper provides initial results from laboratory testing prior to a second round of airborne test flights of the modified LURSOT system. The second part of this paper provides details on a new generation of laser fluorosensor, known as Scanning Laser Environmental Airborne Fluorosensor (SLEAF). SLEAF recently has been installed on Environment Canada's DCS aircraft. SLEAF incorporates a high-power excimer laser, high-resolution range-gated intensified diode-array spectrometer, and a pair of variable speed and angular displacement scanning mirrors. These scanning mirrors provide SLEAF with the across-track sampling pattern needed to detect narrow bands of oil that can pile up along the high tide lines of beaches and shorelines. Ground testing of SLEAF has now been underway for some time. This paper provides details of the sensor installation and testing program, and illustrates the operational capabilities of the new system. It is believed that this new sensor will provide prompt reliable detection and mapping of oil contamination in a variety of marine and terrestrial environments.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".