Remote Detection of Submerged Orimulsion with a Range-Gated Laser Fluorosensor
Bibliographic record
Abstract
ABSTRACT Bituminous fuels (in the form of water-based emulsions) are increasingly being used as fuel sources in many countries. When spilled in a marine environment, these emulsified fuels initially disperse and then, under certain circumstances, coalesce to become highly adhesive to beaches and shorelines. These fuels may either float or submerge, depending on the salinity of the water into which the spill occurs. Similar situations are known to occur with some conventional heavy fuels, as was the case with the Erika incident off the coast of France. Technologies to detect these neutrally buoyant and/or submerged fuels are urgently needed. The remote detection of submerged oil is a daunting task. The majority of sensors commonly used for the detection of surface oil slicks are of no use for the detection of submerged oil. Environment Canada and the Canadian Coast Guard have recently undertaken a series of bench-scale studies to develop technologies for the real-time remote detection of neutrally buoyant and/or submerged fuels in the marine environment. The unique capabilities of “active sensors” such as laser fluorosensors are being evaluated for the subsurface detection of heavy petroleum products. The detection of submerged Orimulsion by laser-induced fluorescence has been demonstrated at a distance of 81 m (265 feet) in a small test tank. Further experiments are underway to confirm the real-time detection of submerged Orimulsion, initially on the ground, and then through airborne tests.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".