Geospatial analysis of the patterns of chemical exposures among biota in the Canadian Oil Sands Region
Bibliographic record
Abstract
Understanding the patterns of chemical exposure among biota across a landscape is challenging due to the spatial heterogeneity and complexity of the sources, pathways, and fate of the different chemicals. While spatially-driven relationships between contaminant sources and biota body burdens of a single chemical are commonly modelled, there has been little effort on modelling chemical mixtures across multiple wildlife species in the Canadian Oil Sands region. In this study, we used spatial principal components analysis (sPCA) to assess spatial patterns of the body burdens of 22 metals and Potentially Toxic Elements (PTEs) in 492 individual wildlife, including fur-bearing mammals, colonial waterbirds, and amphibians collected from the Canadian Oil Sands region in Canada. Spatial analysis and mapping both indicate that some of the complex exposures in the studied biota are distributed randomly across a landscape, which suggests background or non-point source exposures. In contrast, the pattern of exposure for seven metals and PTEs, including mercury, vanadium, lead, rubidium, lithium, strontium, and barium, exhibited a clustered pattern to the east of the open-pit mining area and in regions downstream of oil sands development which indicates point-source input. This analysis demonstrated useful methods for integrating monitoring datasets and identifying sources and potential drivers of exposure to chemical mixtures in biota across a landscape. These results can be used to support an adaptive monitoring program by identifying regions needing additional monitoring, health impact assessments, and possible intervention strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".