The need for adequate quality assurance/quality control measures for selenium larval deformity assessments: Implications for tissue residue guidelines
Bibliographic record
Abstract
Assessing the frequency and severity of larval fish deformities is a subjective exercise that is subject to considerable parameter uncertainty unless appropriate quality assurance/quality control (QA/QC) measures are incorporated. This issue has received limited attention in the literature. Only one study was identified that contained adequate data to evaluate the reproducibility of larval deformity data. Parameter uncertainty was substantially larger than expected. There was poor reproducibility between observers for nearly all types and magnitudes of deformities, and there were particularly large differences in how mild deformities were assessed. The reproducibility of the edema endpoint was the poorest of the 4 types of deformity evaluated. Specific recommendations for improving the QA/QC aspects of larval deformity assessments include blind and nonsequential labeling; explicit effort on the development and application of an a priori framework; internal QC checks to quantify the influence of sample preservatives, observer drift, or multiple observers; and an external QC check of a minimum of 10% of all larval fish. Future selenium reproductive studies should include an explicit uncertainty analysis and disclose raw deformity data to facilitate recalculation of tissue residue guidelines as the science in this area advances.
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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 | 0.251 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".