Assessment of whole effluent toxicity test variability: Partitioning sources of variability
Bibliographic record
Abstract
Abstract In this article, we quantify the variability of toxicity tests used in whole effluent toxicity (WET) testing and ambient water testing and demonstrate how knowledge of this variability can be used in the interpretation of compliance with WET limits in National Pollutant Discharge Elimination System permits. Whole effluent toxicity test endpoint accuracy and precision are important factors in establishing the credibility of test results. Initially, we developed a national data set consisting of raw reference toxicant data from freshwater and marine tests. The data set consisted of the most commonly used test species, protocols, and laboratories and included results from multiple tests over time within single laboratories. Using a random-effects model, we evaluate and estimate the following variance components: between-laboratory variability, variability as a function of dilution concentration, variability of toxicity tests conducted over time, and random error. A variance components model was used to calculate the relative contribution of each variance component to the total variability in specific test endpoints. All analyses were conducted separately for specific reference toxicant, test species, and test protocol combinations. We demonstrate how to use the resulting variance estimates to calculate the minimum significant difference expected for specific test species and test protocols and present an application with WET test data. We present an application using actual WET test results and make recommendations for ensuring the quality of the information resulting from future WET testing.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".