Oil on the beach: A laboratory investigation into the influence of temperature on oil penetration into shoreline sediments
Bibliographic record
Abstract
When oil reaches shorelines during a marine oil spill, it can persist in the sediment for decades, extending well beyond on-water operations and can become a significant expense to the overall response. Oil's persistence on beaches is initially driven by its ability to penetrate sediment, a process heavily influenced by a suite of in situ shoreline conditions as well as the oil's properties. One property of note is viscosity, which undergoes non-linear changes under the influence of temperature and influences oil's ability to penetrate shoreline sediments. Through 435 column experiments, this study systematically examines the penetration behaviour of diluted bitumen (dilbit) and a distillate very low-sulphur fuel oil (VLSFO) in unsaturated, coarse-grained, inorganic beach sediments under seven temperature regimes (Polar to Equatorial), five sediment types and two oil types. Results indicate that the sediment's temperature significantly influences oil penetration, while the presence of temperature gradients within the sediments exerts minimal influence on its penetration. Low temperatures limited oil penetration, concentrating oil near the sediment's surface. In contrast, higher temperatures in those same sediments resulted in deeper penetration and increased the net volume of oil-contaminated sediment. These findings confirm the temperature-viscosity feedback as a key control on oil migration in marine beach sediments and provide new empirical insights that directly inform shoreline response strategies and waste management planning across diverse climatic regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".