SPILLVIEW: A SUPPORT TO DECISION-MAKING SOFTWARE IN EMERGENCY RESPONSE TO MARINE OIL SPILL
Bibliographic record
Abstract
ABSTRACT In the event of a marine oil spill, it is necessary to quickly and clearly assess the situation and estimate the extent of the area potentially impacted by oil. This software combines the following features integrated in a Geographical Information System: Geo-referenced digital aerial survey; Access to trajectory forecast model results; charts with marine and terrestrial data. These features allows a better planning of the emergency response in terms of deployment of personnel and equipment, because it helps to document clearly the observed spill and to give rapidly the length of the coastline at risk and the forecasted time at which the oil spill will start reaching the coast. Aerial surveys are one of the main tools used towards these ends. Aerial observations support the planning of oil cleanup and recovery work, and can also provide accurate data for oil spill fate and trajectory models. Aerial surveyors traditionally use paper maps to record their observations. This way of doing things presents some limits. These include: 1) the difficulty to evaluate the exact location of observed features on the map; 2) the difficulty to record all the necessary information on a fixed-scale map and; 3) the issue of transferring the recorded observations to spill managers, which takes time, requires explanations from the observer and can be subject to interpretation mistakes. These are the reasons why the Canadian Coast Guard, in partnership with Cogeni Technologie Inc., developed the SpillView software system. SpillView, which runs under the Windows XP operating sytem, is designed to operate on a pressure sensitive tablet PC equipped with a GPS and electronic maps. The system displays the real time location and trajectory of the aircraft. The observer can record different types of observations (such as oil location, environmental resources, and shorelines contamination) on georeferenced layers that can be individually exported to formats compatible with other Geographical Information Systems. The observer can also use the system to electronically transfer the observed oil location to a spill modeling center, and display the modeling results within minutes. Spillview proved to be a good tool to support training and exercises, as it can be used to portray different spill scenarios on electronic maps. The software could also be used for other aerial survey needs, such as national security or forest fires. SpillView is presently being enhanced in order to provide operational support by enabling real time access to equipment inventory databases and fieldwork description forms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".