Bibliographic record
Abstract
Oil pollution on the ocean has been highlighted in the past 40 years by several tanker accidents in Europe, like those of Exxon Valdez, Erika or Prestige. But these oil tanker accidents only account for a few percent of the total oil pollution worldwide and hide the regular pollution in important traffic zones like the Mediterranean and other oceans caused by oil drillings or illegal discharges. Due to very regular acquisitions from imaging radar on board satellites since the beginning of the 90's, statistical information about slicks is available all over the world oceans. From the first SAR satellites dedicated to research (ERS series, RadarSat-1) via European Envisat, TerraSAR-X, Cosmo Skymed or Canadian RadarSat-2 to the new European Sentinel series (namely Sentinel 1A, 1B and possibly 1C in the future) oriented towards services and applications, the expertise from the R&D community allows a much better oil spill monitoring nowadays. Oil spills appear as a dark patch on the SAR image. Nevertheless, detecting oil slicks and oil spills remains difficult, as other phenomena are also modifying the sea surface conditions: wind, sea state, currents, ... In order to go beyond oil spill detection and tracking and for setting an efficient prevention system, one must also detect and identify the ships responsible of the oil discharges. For that purpose, synergetic approaches have been used, mixing the radar imaging inputs with met information and mandatory ship identification systems (AIS - Automatic Identification System) mainly. This effort is quite important as the vast majority of these pollutions occur near the coastal zones where 80% of the world population lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".