Persistent Cytotoxicity and Endocrine Activity in the First Oil Sands End-Pit Lake
Bibliographic record
Abstract
Oil sands process-affected water (OSPW) is a byproduct of bitumen extraction that has persistent toxicity owing to its complex mixture of organics. A prominent remediation strategy that involves aging OSPW in end-pit lakes and Base Mine Lake (BML) is the first full-scale test. Its effectiveness over the first 5 years was investigated here using real-time cell analysis, yeast estrogenic and androgenic screens (YES/YAS), and ultra-high-resolution mass spectrometry. HepG2 cytotoxicity per volume of BML organics extracted decreased with age; however, the toxic potency (i.e., toxicity per mass of extract) was not significantly different between years. This was consistent with mass spectral evidence showing no difference in chemical profiles, yet lower total abundance of organics in field-aged samples, suggestive that dilution explains the declining cytotoxicity in BML. The IC 50 ’s of BML extracts for YES/YAS antagonism were at environmental concentrations and were similar despite differences in field-age. Persistent YES/YAS antagonism and cytotoxicity were detected in experimental pond OSPW field-aged >20 years, and while organic acids were depleted here, non-acid chemical classes were enriched compared to BML, suggesting these contribute to persistent toxicity of aged OSPW. To avoid a legacy of contaminated sites, active water treatment may be required to accelerate detoxification of end-pit lakes.
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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".