Relationships among tissues, biofluids, and otolith selenium concentrations in wild female burbot (<i>Lota lota</i>)
Bibliographic record
Abstract
In the Lake Koocanusa-Kootenai River system (Montana, USA and British Columbia, Canada), selenium (Se) contamination has become an international concern and is suspected to contribute to the observed burbot (Lota lota) population collapse. Due to our limited ability to sample burbot in Lake Koocanusa for monitoring studies, we used a reference population to develop tools to model tissue Se disposition for a focal species in systems with elevated Se. Total Se concentrations in otoliths, biofluids (blood and endolymph), and tissues (muscle, liver, and ovary) from burbot in reference lakes in northwestern Ontario, Canada, were measured to document tissue-to-tissue Se relationships and evaluate the potential for otoliths to retrace Se exposure in fish. Among burbot tissue, Se concentrations were the highest in the ovary (mean ± SD = 4.55 ± 2.23 μg g-1 dry mass [dm]), followed by the liver (2.69 ± 1.96 μg g-1 dm) and muscle (1.87 ± 1.14 μg g-1 dm), and decreased with body size (p < 0.05). In otoliths, Se was detected at low levels (<1 μg g-1). Selenium concentrations in burbot samples were positively correlated among muscle, ovary, liver, and endolymph tissues, but not for the most recent annually averaged or lifetime-averaged Se concentrations in otoliths. We hypothesize that Se concentrations were too low in this study to establish links between otoliths and other fish tissues and to detect significant lifetime variation in individuals, and that further validation using archived otoliths from burbot exposed to elevated Se levels in Lake Koocanusa-Kootenai River is needed to reconstruct exposure histories. However, intercompartmental models proved valuable for estimating Se concentrations in burbot tissues only available by means of lethal sampling (i.e., ovary), although additional work should confirm whether the established models are reliable to predict concentrations in Se-impaired systems as tissue distributions are likely to differ with increasing Se levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".