Evaluation of Nonlethal Methods for the Analysis of Mercury in Fish Tissue
Bibliographic record
Abstract
Abstract Thousands of fish are sacrificed each year to determine potential human exposure to mercury (Hg) from fish consumption. In this paper, we use lake whitefish Coregonus clupeaformis and northern pike Esox lucius to demonstrate that accurate and reliable measures of fish muscle Hg concentrations can be determined from small samples (<100 mg) harvested with biopsy tools. Reliability of results primarily depends upon analytical methodology and tissue sample weight. Mercury concentrations estimated by use of cold‐vapor atomic absorption spectrophotometry (CVAAS) on small composite tissue samples harvested with a Tru‐Cut (TC) biopsy needle (mean sample wet weight = 47 mg) were less precise than estimates from tissue samples harvested with a dermal punch (DP; mean sample wet weight = 126 mg). Precision differences presumably occurred because TC samples weighed less than the prescribed minimum weight (>100 mg) for CVAAS. There was no difference in precision of Hg concentrations among tissue extraction methods when biopsy samples were analyzed via cold‐vapor atomic fluorescence spectrophotometry (CVAFS). Mean tissue Hg concentrations obtained with the biopsy techniques and CVAAS or CVAFS were similar to benchmark concentrations in fillet samples (within 6%), even for TC– CVAAS. A field study of the effects of the DP biopsy method on survival of northern pike showed that tissue harvesting did not reduce survival. Our results clearly demonstrate that analysis of Hg content in muscle harvested with biopsy tools provides Hg measures comparable in accuracy to traditional, whole‐fish methods but without causing mortality.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".