Assessing the Capability of Remote Detection Systems to Identify Oil Slicks in Harbours
Bibliographic record
Abstract
ABSTRACT In the past, the presence of oil slicks in harbours has been acknowledged as a natural result of ship movement and industrial activity in the area. In recent years, such pollution has become socially and environmentally unacceptable. The most common method of detecting the presence of oil in a harbour is currently the use of marine patrols and visual observations. This is a costly activity, which is not very effective. Harbourmasters and others involved in the operation of major marine facilities have recognized the need for a system that would continuously monitor for the presence of oil on water, and report its location to a central control room. In order to prosecute such violations, the system should be capable of identifying the oil and relating the oil to a specific ship. In the last ten years there have been significant developments in the remote sensing of oil on water in the support of oil-spill response. Such systems have generally been used as airborne packages, which allow the coverage of large areas. For the harbour situation, the area of coverage is fixed permitting the sensors to be mounted on towers. It would be ideal if the system had coverage similar to that of existing Vessel Traffic Systems (VTS). This paper will examine the applicability of using existing and proven oil-spill detection systems for harbours.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".