Analysis of Selenium in Fish Tissue: An Interlaboratory Study on Weight Constraints
Bibliographic record
Abstract
Environmental monitoring programs that target fish tissues for selenium (Se) analysis present unique sampling and analytical challenges. Selenium monitoring programs ideally focus on egg/ovary sampling but frequently sample multiple tissues with varying lipid content, often target small-bodied fish species because of their small home ranges, and require reporting in units of dry weight. In addition, there is a growing impetus for nonlethal tissue sampling in fish monitoring. As a result, Se monitoring programs often generate low-weight tissue samples of varying lipid content, which challenges analytical laboratories to quantify tissue Se concentrations accurately, precisely, and at desired detection limits. The objective of the present study was to stress-test some conventional analytical techniques used by commercial laboratories in terms of their ability to maintain data quality objectives (DQOs) in the face of sample weight constraints. Four laboratories analyzed blind a suite of identical samples, and data were compared against a priori DQOs for accuracy, precision, and sensitivity. Data quality tended to decrease with decreasing sample weight, particularly when samples were less than the minimum weights requested by the participating laboratories; however, effects of sample weight on data quality were not consistent among laboratories or tissue types. The present study has implications for accurately describing regulatory compliance in Se monitoring programs, highlighting some important considerations for achieving high data quality from low-weight samples. Environ Toxicol Chem 2023;42:2119-2129. © 2023 SETAC.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".