Fate of Crude Oil in the Environment and Remediation of Oil Spills
Bibliographic record
Abstract
The world consumes approximately 5.1 billion tons of crude oil per year, with the United States and Saudi Arabia producing the largest shares [1]. Countries rely on various means for transporting crude oil [1, 2]. Large vessel/tankers transport oil at sea, while oil is transported inland via pipelines, railroads, trucks, and barges [2]. Unfortunately, some of the oil gets spilled into the ocean, freshwater bodies, and terrestrial ecosystems during its production, transportation, and use [3]. Usually, oil spills are caused by accidents involving tankers, barges, pipelines, refineries, drilling rigs, and storage facilities [3]. Small spills are frequent, but are handled by local responders. However, in the case of relatively large spills, known as spills of national significance, a national effort is needed to respond. Examples of large spills include the running aground of the Exxon Valdez in Alaska and the Deepwater Horizon blowout in the Gulf of Mexico. These spills triggered the application of the Oil Pollution Act of 1990 and ensuing regulations [4-9]. When an oil spill reaches the shoreline, efforts are taken to remove as much of the oil as possible using physical means, such as water flushing [3]. When the oil content within sediments becomes too low, physical removal becomes inefficient and/or can lead to further damage [3]. In this situation, oil biodegradation, that is the degradation of oil mediated by microorganisms, becomes an important process to consider [10]. Beaches are bioremediated by monitoring and enhancing the biodegradation of oil. Critically understanding and analyzing oil biodegradation and remediation techniques allows for a better response by decision-makers. This paper first addresses the general chemical composition of oils and then covers the different physical and natural processes that can remove crude oil from beaches, with a focus on bioremediation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".