Spatial patterns and environmental factors related to arsenic bioaccumulation in boreal freshwater fish
Bibliographic record
Abstract
To better understand the spatial patterns in arsenic (As) bioaccumulation in freshwater systems, we investigated ecological, physical, and chemical factors associated with total arsenic concentrations ([As]) in lacustrine and riverine fish across Ontario, Canada, using a dataset of 3200 fish across 152 waterbodies. Assembled data of water chemistry, landscape characteristics, and stable carbon and nitrogen isotope ratios in muscle tissue were then used to assess factors related to As bioaccumulation. Results show that [As] were generally low across most species and waterbodies (i.e., <1 µg·g −1 wet in many inland fish). However, fish from northern coastal rivers had up to 23-fold higher [As] when compared with fish from landlocked sites. As concentrations increased slightly with the proportion of pelagic carbon in a fish's diet, although relationships varied among species and sites. Furthermore, principal component scores, representing landscape and water chemistry variables, were related to [As] in fish, but these relationships varied among species. These results will help improve the efficacy of fish contaminant monitoring by further identifying key physical and ecological variables related to higher [As] in fish.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".