Bioremediation of Petroleum Hydrocarbons in the Upper Parts of Sandy Beaches
Bibliographic record
Abstract
The biodegradation of dispersed crude oil in the ocean is relatively rapid (a half-life of a few weeks). However, it is often much slower on shorelines, usually attributed to low moisture content, nutrient limitation, and higher oil concentrations in beaches than in dispersed plumes. Another factor may be the increased salinity of the upper intertidal and supratidal zones because these parts of the beach are potentially subject to prolonged evaporation and only intermittent inundation. We have investigated whether such an increase in salinity has inhibitory effects on oil biodegradation in seashores. Lightly weathered Hibernia crude oil was added to beach sand at 1 or 10 mL/kg, and fresh seawater, at salinities of 30, 90, and 160 g/L, was added to 20% saturation. The biodegradation of oil was slower at higher salinities, where the half-life increased from 40 days at 30 g/L salts to 58 and 76 days at 90 and 160 g/L salts, respectively, and adding fertilizers somewhat enhanced oil biodegradation. Increased oil concentration in the sand, from 1 to 10 mL/kg, slowed the half-life by about 10-fold. Consequently, occasional irrigation with fertilization could be a suitable bioremediation strategy for the upper parts of contaminated beaches. However, dispersing oil at sea is probably the most suitable option for the optimal removal of spilled crude oil from the marine environment.
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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) | 0.001 | 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 | 0.000 |
| 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".