Effects of oil characteristics on the performance of shoreline response operations: A review
Bibliographic record
Abstract
Marine oil spills are serious ecological disasters that have massive adverse impacts on the environment. The impacts are even worse once the spilled oil is stranded on a shoreline. A series of shoreline cleanup methods are deployed to remove spilled oil, but their performance can be affected by the stranded oil. This review therefore comprehensively investigates the characteristics of spilled oil on the shoreline and explores their effects on the effectiveness of shoreline response operations. First, the five basic groups of spilled oil (i.e., non-persistent light oils, persistent light oils, medium oils, heavy oils, and sinking oils) are discussed and each oil fraction is introduced. Three distribution scenarios of adhered oil on shorelines are also analyzed. The effects of oil characteristics, such as oil type, viscosity, evaporation, and composition, on the performance of chemical treatments, physical methods, and biodegradation are then discussed and analyzed. Finally, the article provides recommendations for future research on aspects of shoreline oiling prevention, quick responses, response tool sets, and other considerations, which may have significant implications for future decision-making and the implementation of shoreline cleanup to effectively remove stranded oil.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".