An International Perspective on the Tools and Concepts for Effluent Toxicity Assessments in the Context of Animal Alternatives: Reduction in Vertebrate Use
Bibliographic record
Abstract
Since the 1940s, effluent toxicity testing has been used to assess potential ecological impacts of effluents and help determine necessary treatment options for environmental protection prior to release. Strategic combinations of toxicity tests, analytical tools, and biological monitoring have been developed. Because the number of vertebrates utilized in effluent testing is thought to be much greater than that used for individual chemical testing, there is a new need to develop strategies to reduce the numbers of vertebrates (i.e., fish) used. This need will become more critical as developing nations begin to use vertebrates in toxicity tests to assess effluent quality. A workshop was held to 1) assess the state of science in effluent toxicity testing globally; 2) determine current practices of regulators, industry, private laboratories, and academia; and 3) explore alternatives to vertebrate (fish) testing options and the inclusion of modified/new methods and approaches in the regulatory environment. No single approach was identified, because of a range of factors including regulatory concerns, validity criteria, and wider acceptability of alternatives. However, a suite of strategies in a weight-of-evidence approach would provide the flexibility to meet the needs of the environment, regulators, and the regulated community; and this "toolbox" approach would also support reduced reliance on in vivo fish tests. The present Focus article provides a brief overview of wastewater regulation and effluent testing approaches. Alternative methodologies under development and some of the limitations and barriers to regulatory approaches that can be selected to suit individual country and regional requirements are described and discussed. Environ Toxicol Chem 2018;37:2745-2757. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of 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 | 0.024 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".