The efficacy of scale sampling for monitoring trace element concentrations and stable isotopes in commercially harvested walleye (<i>Sander vitreus</i>)
Bibliographic record
Abstract
Commercial and sport fishes are subject to rigorous monitoring for concentrations of elements that could pose threats to human health, with numerous advisories issued by authorities annually for those fisheries with high mercury (Hg) concentrations. In Lake Winnipeg, Manitoba, Canada, the commercial walleye fishery is valued at more than $20 million/year, but has historically been subject to Hg advisories. We used an information theoretic approach to evaluate the utility of non-destructive fish-scale sampling to predict As, Mn and Hg concentrations, as well as stable isotope values in walleye muscle by analysing paired samples. Hg concentrations in scales were significantly related to those in muscle (r(2) = 0.75), but the relationships were weaker for As and Mn. The δ(15)N values in scales predicted δ(15)N in muscle reasonably well (r(2) = 0.72), whereas scale δ(13)C and δ(34)S had less predictive power for estimating their respective muscle stable isotope values. For all three isotope values, sex was a marginal predictor, with parameter confidence intervals bounding zero. Analytical constraints currently limit the utility of non-destructively analysing scales for Hg, but hindcasting trophic changes using archived walleye scales may be useful in understanding shifts in nutrients and production, particularly in impacted lake systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".