Species sensitivity distribution evaluation for selenium in fish eggs: Considerations for development of a Canadian tissue-based guideline
Bibliographic record
Abstract
A freshwater Se guideline was developed for consideration based on concentrations in fish eggs or ovaries, with a focus on Canadian species, following the Canadian Council of Ministers of the Environment protocol for developing guideline values. When sufficient toxicity data are available, the protocol recommends deriving guidelines as the 5th percentile of the species sensitivity distribution (SSD). When toxicity data are limited, the protocol recommends a lowest value approach, where the lowest toxicity threshold is divided by a safety factor (e.g., 10). On the basis of a comprehensive review of the current literature and an assessment of the data therein, there are sufficient egg and ovary Se data available for freshwater fish to develop an SSD. For most fish species, Se EC10 values (10% effect concentrations) could be derived, but for some species, only no-observed-effect concentrations and/or lowest-observed-effect concentrations could be identified. The 5th percentile egg and ovary Se concentrations from the SSD were consistently 20 µg/g dry weight (dw) for the best-fitting distributions. In contrast, the lowest value approach using a safety factor of 10 would result in a Se egg and ovary guideline of 2 µg/g dw, which is unrealistically conservative, as this falls within the range of egg and ovary Se concentrations in laboratory control fish and fish collected from reference sites. An egg and ovary Se guideline of 20 µg/g dw should be considered a conservative, broadly applicable guideline, as no species mean toxicity thresholds lower than this value have been identified to date. When concentrations exceed this guideline, site-specific studies with local fish species, conducted using a risk-based approach, may result in higher egg and ovary Se toxicity thresholds.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".