Influence of heavy Canadian crude oil on pristine freshwater boreal lake ecosystems in an experimental oil spill
Bibliographic record
Abstract
The overall impact of a crude oil spill into a pristine freshwater environment in Canada is largely unknown. To evaluate the impact on the native microbial community, a large-scale in situ model experimental spill was conducted to assess the potential role of the natural community to attenuate hydrocarbons. A small volume of conventional heavy crude oil (CHV) was introduced within contained mesocosm enclosures deployed on the shoreline of a freshwater lake. The oil was left to interact with the shoreline for 72 h and then free-floating oil was recovered using common oil spill response methods (i.e. freshwater flushing and capture on oleophilic absorptive media). Residual polycyclic aromatic hydrocarbon (PAH) concentrations returned to near preoiling concentrations within 2 months, while the microbial community composition across the water, soil, and sediment matrices of the enclosed oligotrophic freshwater ecosystems did not shift significantly over this period. Metagenomic analysis revealed key polycyclic aromatic and alkane degradation mechanisms also did not change in their relative abundance over the monitoring period. These trends suggest that for small spills (<2 l of oil per 15 m2 of surface freshwater), physical oil recovery reduces polycyclic aromatic hydrocarbon concentrations to levels tolerated by the native microbial community. Additionally, the native microbial community present in the monitored pristine freshwater ecosystem possesses the appropriate hydrocarbon degradation mechanisms without prior challenge by hydrocarbon substrates. This study corroborated trends found previously (Kharey et al. 2024) toward freshwater hydrocarbon degradation in an environmentally relevant scale and conditions on the tolerance of residual hydrocarbons in situ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".