The Freshwater Oil Spill Remediation Study (FOReSt): 2018 Pilot Study at the Experimental Lakes Area, Canada
Bibliographic record
Abstract
ABSTRACT (#667537) Monitored natural recovery (MNR) was assessed as a non-invasive method for limiting residual oil exposure in the aquatic environment following contained spills of Cold Lake Blend diluted bitumen (CLB) and conventional heavy crude (CHV) at the IISD-Experimental Lakes Area in Canada. Oils were applied and left in place for 72h to simulate potential spill cleanup response times. After physical removal of free surface oil, biological response and recovery (microbes, zooplankton communities, emergent insects, and benthic invertebrate) was assessed over 80d and exposure of polycyclic aromatic compounds (PACs) and their alkylated forms (aPACS) in water and sediment were characterized. Embryonic development of fathead minnow eggs exposed to water from each of the enclosures was used to determine potential impacts on fish early life stage development. There were significantly different concentrations of PACs in the enclosures treated with diluted bitumen and CHV immediately after application and attenuation differed between the two products throughout the study period. Water contained primarily 3 ring PACs and aPACs. Microbial taxa with known oil degrading capacity increased in water relative to total community abundance. Emergent insect abundance was significantly lower in both oil treated enclosures relative to reference enclosures, but fish development was not significantly impacted by oil treatments. Monitored natural recovery could be successfully applied to oil spill affected freshwater shorelines, but additional data are required to determine long term recovery trajectories.
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 | 0.002 | 0.001 |
| 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".