Selenium and mercury tissue partitioning and trophodynamics in the Lake Koocanusa (USA–Canada) fish community
Bibliographic record
Abstract
ABSTRACT: Mining-related contaminants such as selenium (Se) and mercury (Hg) accumulate in aquatic organisms and transfer through aquatic food webs, where they can exert toxic effect undermining the ecological health of aquatic resources. Yet, how Se and Hg co-distribute within food webs and within individual organisms remains poorly understood. We compiled muscle and ovary Se and Hg concentration data from fishes captured in Lake Koocanusa—a North American reservoir receiving elevated Se and Hg inputs from coal mining operations in the Elk River Valley, Canada—to provide insights into the co-processing of Se and Hg within fish and among fish species, including how ecological or seasonal factors shift these distribution patterns. Se and Hg concentrations (ranges: 0.21-38.7 μg Se g -1 dry weight and 0.01-0.84 μg Hg g -1 wet weight), as well as Se:Hg molar ratios, varied substantially between tissues and among species, reflecting biological and ecological differences. Our results suggest that the decoupling of Se or Hg deposition in fish muscle versus ovary may reflect different pathways of impairment, at the individual and population levels, and that interspecific and tissue-specific variability makes it challenging to set a universal Se:Hg threshold for the protection of aquatic organisms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".