A Response Guide for Sunken Oil Mats (SOMs): Formation, Behavior, Detection, and Recovery
Bibliographic record
Abstract
Based on past spills, the conditions under which floating oil mixes with enough sand to form sunken oil mats (SOMs) are identified. SOMs form mostly during spills of heavy crudes or heavy fuel oils, but also highly weathered and viscous crude oils. They usually form when oil and sand are both suspended in the water column by breaking waves or by the erosion of heavily oiled sand from the beach. The oil–sand mixture needs an area in the nearshore where it can accumulate into mats, such as in troughs or inside lagoons, where wave energy is reduced. SOMs can be oily (>40% oil) or sandy (<<40% oil), with oily SOMs posing risks of the oil separating from the sand and refloating. Methods are described for SOMs detection and recovery based on their location, in either the surf zone or the open-water environment seaward of the surf zone. A matrix provides guidance on methods based on effectiveness and environmental impacts for different site conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.117 | 0.123 |
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".