Bibliographic record
Abstract
The response to a major oil spill can be challenging in temperate climates and with good weather conditions. By contrast, a major spill in or under ice and snow, presents a whole new series of challenges. This paper reviews detection technologies for these challenging situations. A number of acoustic techniques have been tried in test tank situations and it was found that acoustic detection of oil was possible because oil behaves as a solid in acoustic terms and transmits shear waves. Laboratory tests have been carried out and a prototype was built and tested in the field. Radio frequency methods, such as ground penetrating radar (GPR), have been tested for both oil-under-ice and oil-under-snow. The GPR method does not provide sufficient discrimination for positive oil detection in actual spills. Preliminary tests on the use of Nuclear Magnetic Resonance for detecting oil, in and under ice, shows promise and further work on this is being done at this time. A number of other oil-in-ice detection technologies have been tried and evaluated, including standard acoustic thickness probes, fluorosensor techniques, and augmented infrared detection. Each of these showed potential in theory during tank tests. Further testing on these proposed methods is required.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".