RADAR IMAGES IN THE PROSECUTION OF ILLEGAL OIL DISCHARGES: \nOPPORTUNITIES AND A CASE STUDY
Bibliographic record
Abstract
Illegal oil discharges from ships are harmful to the world’s oceans. Earth observation satellites \nsuch as Synthetic Aperture Radar (SAR) offer many advantages in the collection of data for use \nin the prosecution of illegal discharges. However, the process by which radar images can be used \nin court is yet to be ascertained, especially with regards to the admissibility and authentication of \nthe data as evidence. It was determined that expert witness qualifications and the reliability of \nSAR images for oil spill detection address the concept of admissibility of the information \npresented in court. Conversely, authentication relies on quality metadata. A case study is \npresented that uses a RADARSAT-1 (R-1) SAR image as the main evidence and oblique aerial \nphotographs as supporting documentation of an offshore oil spill incident in the waters south of \nNewfoundland and Labrador, Canada. This case helps highlight the legal chain of custody \ninvolved with using remote sensing images. The research reveals that satellite SAR imagery can \nbe used operationally to extract information about oil spills and the ocean environment. The \nmain difficulties with the use of these images in the prosecution of illegal oil discharges lie with \ntracking the analysis process, the coordination of aerial photograph recording as supporting \nevidence and the overall evidence gathering protocol.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.005 |
| 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".