Restoration of an Oil-Contaminated St. Lawrence River Shoreline: Bioremediation and Phytoremediation
Bibliographic record
Abstract
ABSTRACT The purpose of this field study was to evaluate bioremediation and phytoremediation in restoring an oil-contaminated freshwater shoreline. Weathered Mesa light crude oil was released intentionally onto small plots in the upper intertidal zone of a study site located along the St. Lawrence River. Treatments were established to examine the effect of nutrient addition and the role of plants (Scirpus pungens) on the removal of oil constituents from the contaminated plots. Fertilizers under evaluation included sodium nitrate, prilled ammonium nitrate, and triple super phosphate. Composite core samples were collected after 0, 1, 2, 4, 8, 12, 16, and 21 weeks for identification of remaining oil constituents by gas chromatography-mass spectrometry (GC-MS). To account for differences because of physical washout, all oil constituents were normalized to the conservative biomarker hopane. Although bioremediation and phytoremediation treatments achieved slightly better degradation of hydrocarbons than natural attenuation, no statistically significant evidence of stimulation through addition of nutrients or biodegradation enhancement by vegetation was observed. After 21 weeks, reduction of target parent and alkyl-substituted polycyclic aromatic hydrocarbons (PAHs) averaged 32% in all treatments. Reduction of target alkanes was of similar magnitude. The pattern of disappearance of hydrocarbons was characteristic of biodegradation: namely, the lower molecular weight alkanes declined to a greater extent than the higher carbon-number alkanes, as did the lower molecular weight PAHs compared to the higher molecular weight PAHs. Since there was little evidence supporting enhancement of biodegradation by nutrient addition with and without vegetation, it was concluded that oxygen limitation most likely dominated the persistence of oil hydrocarbons on the oil-contaminated plots.
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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 | 0.000 |
| 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".