INTRA- AND INTERTREATMENT VARIABILITY IN REFERENCE TOXICANT TESTS: IMPLICATIONS FOR WHOLE EFFLUENT TOXICITY TESTING PROGRAMS
Bibliographic record
Abstract
Wole effluent toxicity tests are used in permitting programs across the United States to determine whether effluents are potentially toxic to aquatic biota in receiving environments. In cases where whole effluent toxicity tests indicate unacceptable toxicity, corrective measures or further testing (e.g., field tests) may be required. To be consistent and fair to permit holders, whole effluent toxicity test outcomes (e.g., pass or fail) should not be strongly influenced by intra- and interlaboratory variability. In this study, we quantified intra- and interlaboratory variability for four species–data type combinations using the results of reference toxicant tests compiled from many laboratories in recent years. For each set of test results, we conducted a regression analysis using the generalized linear models framework. The results indicated that the coefficient of variation (CV) for intralaboratory 25% effective concentration (i.e., EC25) values varied from 15.7% for number of young of Ceriodaphnia dubia in laboratory CD4 to 122% for mortality of Menidia beryllina (inland silverside) in laboratory MB3. Interlaboratory variability was small for both mortality (CV = 17.3%) and number of young (CV = 13.4%) of C. dubia. Interlaboratory variability for mortality (CV = 65.8%) and biomass (CV = 117%) of M. beryllina, however, was very high. Our study shows that permit toxicity limits can be exceeded because of factors other than effluent toxicity, particularly when the limits are based on testing of M. beryllina.
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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 | 0.061 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".