Sublethal health effects in laboratory rodents from environmentally relevant exposures to oil sands contaminants
Bibliographic record
Abstract
Increasing activity of oil sands extraction and processing in northern Alberta is marked by ongoing controversy about the nature and extent of associated environmental impacts. Bitumen contains a mixture of toxic chemicals, including metals and residual polycyclic aromatic hydrocarbons (PAHs), whose release into the environment poses a distinct risk to the surrounding environment, plus wildlife and human health. In the present study, the authors evaluated several subclinical biomarkers of exposure and effect to mixtures of metals (Pb, Cd, and Hg) and/or PAHs (3 alkylated forms) at environmentally relevant concentrations (100-fold and 10-fold higher than the maximum dissolved concentrations found in snow, to simulate a worst-case scenario), using laboratory mice as a model for future studies of small mammals in the wild. Both metals and alkyl-PAHs exposure were associated with 1) increased relative liver, kidney, and spleen size; 2) alterations in the homeostasis of the antioxidant vitamins A and E in liver; and 3) compromised glutathione redox status in testes, with results also indicating synergistic interactions from co-exposure. The combination of morphometric and oxidative stress biomarkers provide reliable and sensitive measures of the response to contaminant exposure in a mammalian model, suggesting associated physiological costs. Based on the present experimental study, the authors propose that wild small mammals will prove to be valuable sentinel species reflecting sublethal health effects from oil sands-related contaminants. The present study's results also present a basis for the interpretation of future field data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".