Are Vertebrates Still Needed in Routine Whole Effluent Toxicity Testing for Oil and Gas Discharges?
Bibliographic record
Abstract
Abstract Routine whole effluent toxicity (WET) testing is commonly used to monitor effluent discharges for regulatory compliance in North America. However, the use of fish in WET testing raises ethical concerns and therefore an important question to be explored is whether invertebrates can be used to reduce and/or replace the need for vertebrate testing. The present study evaluated WET data collected for regulatory compliance between 2003 and 2019 (n = 2581 endpoints) from 20 different stationary onshore and offshore oil and gas facilities located across Canada and the United States. Our objective was to assess the relative sensitivity between vertebrates (i.e., fish) and invertebrates in paired samples and to evaluate trends in WET compliance. Despite the variability in testing endpoints, invertebrates displayed equal to or greater sensitivity to tested effluents than fish. For example, based on no-observed-effect concentrations for survival and growth, Americamysis bahia was found to be protective of Menidia beryllina in 90% of endpoint comparisons (n = 336). The results also indicated that regulatory compliance was high (94–100%), with most WET tests passing the established criteria by large margins (79–251%). The results of this comprehensive analysis of historical WET data can be used to improve future permit testing requirements and help answer the question of whether fish tests are needed for routine WET testing. Environ Toxicol Chem 2021;40:1255–1265. © 2020 Shell Oil Company. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. Abstract Historical whole effluent toxicity (WET) data collected for effluent discharge permits from oil and gas facilities were compiled into a database and analyzed. The objective was to explore the relative sensitivities of vertebrate (fish) and invertebrate test organisms in WET tests and to understand performance of oil and gas effluent discharges over time against WET criteria. These data can be used to support discussions on whether there could be opportunities to optimize WET testing such as reduce routine WET testing using vertebrate organisms. M. beryllina = Menidia beryllina; A. bahia = Americamysis bahia; O. mykiss = Oncorhynchus mykiss; D. magna = Daphnia magna; P. promelas = Pimephales promelas; C. dubia = Ceriodaphnia dubia; D. pulex = Daphnia pulex.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".