A statistical evaluation of the safety factor and species sensitivity distribution approaches to deriving environmental quality guidelines
Bibliographic record
Abstract
Abstract The species sensitivity distribution (SSD) distribution approach to estimating water quality guidelines (WQGs) is the preferred method in all jurisdictions reviewed (Australia, Canada, New Zealand, Organisation for Economic Co-operation and Development [OECD] members, South Africa, United States) and is one of the recommended methods for European Commission members for 33 priority and priority hazardous substances. In the event that jurisdiction-specific criteria for data quality, quantity, and taxonomic representation are not met, all of these jurisdictions endorse the use of additional safety factors (SFs) applied to either the SSD-based WQG or, the lowest suitable toxicity test endpoint. In Canada, the British Columbia Ministry of Environment endorses this latter approach as the preferred approach in the belief that so-derived WQGs are more protective than SSD-based WQGs. The level of protection afforded by the latter SF approach was evaluated by statistically sampling minima from random samples of the following distributions: normal, Gumbel, logistic, and Weibull, using a range of coefficients of variation (cVs) and applying the SFs of 2 or 10 used in British Columbia. The simulations indicate that the potentially affected fraction of species (PAF) can be as high as 20%, or, approach 0%. The PAF varies with sample size and CV. Because CVs can vary systematically with mode of toxic action, the PAF using SF-based WQGs can also vary systematically with analyte class. The varying levels of protection afforded by SF-based WQGs are generally inconsistent with the common water quality management goal that allows for a small degree of change under long-term exposure. The findings suggest that further efforts be made to develop high-quality WQGs that support informed decision making and are consistent with the environmental management goal instead of using SFs in the hope of achieving an acceptable but unknown, degree of environmental protection. Integr Environ Assess Manag 2016;12:380–387. © 2015 SETAC Key Points Application of safety factors to the “lowest available” toxicity test endpoint is either preferred or fall-back method for estimating environmental quality guidelines. It is believed that the safety factor method is more protective than other methods such as using the 5th percentile of a species sensitivity distribution. The level of protection using the safety factor method is inconsistent, varying with dataset sample size, degree of variability, and the underlying statistical distribution of the toxicity test endpoint. The safety factor approach can protect (as a limit) 100% of species or less than 55% of species.
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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 | 0.103 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".