Guidelines on the Use of Dispersants for Combating Oil Pollution at Sea
Bibliographic record
Abstract
The Guidelines provide up-to-date information on the use of oil spill dispersants for combating oil spills at sea. They are intended primarily for use by Member Governments and other oil spill responders and should be read with the Manual on Oil Pollution, section IV: Combating Oil Spills.(IA569E). The decision to review the existing IMO guidelines on the use of oil spill dispersants was taken at the 61st session of the Marine Environment Protection Committee (MEPC) of IMO. France, in cooperation with Canada agreed to act as lead country through Centre de Documentation Recherché et Expérimentations sur les pollutions accidentelles des eaux (Cedre) and Department Fisheries and Oceans (DFO). The guidelines are divided into four independent parts addressing different issues. Each part has been developed with a specific objective and aimed at different end-users: Part I – Basic Information on Dispersants and Their Application; Part II – Outline for a National Policy on the Use of Dispersants: Proposed Template for National Policy for the Use of Dispersants; Part III – Operational and Technical Sheets for Surface Application of Dispersants; and Part IV – Sub-sea Dispersant Application.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.060 |
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".