Bioremediation of oil-contaminated coastal freshwater and saltwater wetlands
Bibliographic record
Abstract
Two field studies involving intentional releases of crude oil onto a freshwater wetland and a salt marsh were conducted in Canada in the summers of 1999and 2000, respectively. The objective of both studies was to determine the role of nutrients in enhancing wetland restoration in the presence and absence of wetland plants, The experiments involved several replications of the following oiled treatments: (1) natural attenuation, (2) ammonium nitrate addition with intact plants, (3) ammonium nitrate addition with plants cut back to suppress plant activity, and (4) sodium nitrate addition to separate the effects of ammonium-N from nitrate-N. A fertilized, unoiled treatment was also included. For the salt marsh study, tilling was added as another treatment. Time series data from both studies were analyzed by GC/MS to monitor oil degradation. Results from both field experiments indicate that significant alkane and PAH biodegradation occurred (more so in the salt marsh). Biodegradation rates were not enhanced by any of the amendments in the freshwater wetland experiment, but substantial restoration of the wetland ecosystem was accelerated in the amended treatments. Significant treatment effects were observed in the salt marsh study in regards to alkane but not PAH degradation, and levels of restoration similar to those observed in the freshwater wetland were not evident in the salt marsh ecosystem.
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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) | 0.000 | 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".