New Technique for Estimating Thresholds of Toxicity in Ecological Risk Assessment
Bibliographic record
Abstract
The use and utility of the no observed effect concentration (NOEC) in ecological risk assessment is a contentious issue. One concern is that the NOEC is not representative of a concentration at which no biologically significant effect is occurring. A new method has been developed to estimate the threshold of toxicity, or a true NOEC, for aquatic plants. The method involves determining the effective concentration (ECx) of a number of endpoints from one species. These ECx values are plotted on a log-probability scale. The x-intercept, or a low centile, of the distribution can be interpreted as the threshold of toxicity for that plant at that response level. This threshold is the concentration at which no effects should be observed for any endpoint above that response level. It is based on the assumptions that multiple effect measures from a single species will be log-normally distributed and thatthe distribution contains all possible endpoints for that species. The thresholds and the distributions can then be used as a substitute for the NOEC or ECx in risk assessment techniques, such as hazard quotients and probabilistic ecological risk assessment. This new method of estimating toxicitythresholds is more realistic than the use of arbitrary uncertainty factors, is more conservative than current probabilistic risk assessment methods, allows for simple comparison between species and exposure duration to a toxicant, and may be useful for assessing mixture toxicity. This technique was applied to field derived data with Lemna gibba, Myriophyllum spicatum, and M. sibiricumto assess potential risks from monochloroacetic acid (MCA). Using this new risk assessment method, we conclude that MCA does not appear to pose a risk to aquatic macrophytes under field conditions at current environmental concentrations.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".