Snails as Biomonitors of Oil-Spill and Bioremediation Strategies
Bibliographic record
Abstract
Aquatic and pulmonate snails were evaluated for their suitability as biomonitors of habitat recovery following an experimental oil spill in a freshwater marshland. The mystery snail, Viviparus georgianus, and the mimic pondsnail, Pseudosuccinea columella, were used as sediment quality biomonitors for a controlled oil-spill experiment at a wetland site along the St. Lawrence River (Ste. Croix, Quebec) to assess the impacts of crude oil, rates of natural recovery, and the efficacy of bioremediation treatments to enhance the bacterial degradation of residual oil in the sediments. Sediments from control sites and oiled sites with or without the application of fertilizers as bioremediation strategies, were evaluated both in situ and under controlled laboratory conditions at various time intervals. Snail survival, growth, and histopathological changes were monitored. While V. georgianus proved to be good biomonitors, P. columella appeared unaffected by the treatments. The differing sensitivity may depend on the gastropods' feeding habits. V. georgianus being a detritivore assimilated contaminants from the sediments, while P. columella, being an herbivore, did not directly assimilate contaminants. Nevertheless, snails show potential as important and ideal “tools” for testing environmental conditions because of their abundance, ease of collection, wide distribution, and relatively sedentary nature.
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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".