Patterns and drivers of arsenic bioaccumulation in boreal freshwater fish of Ontario, Canada
Bibliographic record
Abstract
Wild fish consumption can be an important pathway for metal exposure to subsistence and recreational fishers. Elevated levels of arsenic (As) have been reported by monitoring programs and previous research in several fish species in the province of Ontario, Canada. This is of particular concern for First Nation communities in remote northern areas that rely on locally sourced freshwater fish for subsistence. However, provincial monitoring for As in fish is less extensive than for other contaminants (e.g. mercury) and less is known about how As behaves in aquatic systems under various conditions. The goal of this thesis was to improve understanding of patterns in As accumulation across freshwater systems. More specifically, I investigated the spatial variability of total As in fish muscle and its ecological, physical and chemical drivers in lakes and rivers across Ontario. To do this, I amalgamated As data from previous research and a long-term contaminant monitoring program, resulting in a dataset of total arsenic concentrations ([As]) in 3200 fish across 30 species and 152 waterbodies sampled between 2008 and 2018. Additional datasets of water chemistry parameters (e.g., pH, DOC), landscape variables (e.g., geology, watershed area), and stable carbon and nitrogen isotopes (measures of fish trophic ecology) were also amassed from governmental and open-source databases to examine the influence of these variables on As bioaccumulation in fish. Results show that [As] were generally low across most fish species and most waterbodies sampled. However, fish from large northern rivers draining into the ocean had up to 23-fold higher concentrations of As compared to fish from landlocked sites. In general, [As] increased slightly with fish size, although relationships varied among fish species and sites. Evidence of biomagnification of As across fish species was also observed in several lake sites. Furthermore, principal component scores, representing landscape and water chemistry variables, were related to [As] in fish, but the relationships varied among species. These results will help improve the efficacy of fish contaminant monitoring in freshwater systems by identifying physical and ecological variables related to higher concentrations of As in fish while also emphasizing the value of repurposing existing datasets and utilizing open data sources.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".