Deriving predicted no-effect concentrations (PNECs) using a novel assessment factor method
Bibliographic record
Abstract
Ecological risk assessments of substances with limited ecotoxicity datasets typically employ an assessment factor (AF) method to determine a predicted no-effect concentration (PNEC) needed for risk characterization. A PNEC is usually derived by dividing the lowest toxicity value in the substance’s dataset by a certain assessment factor. The AF method, in its various iterations, has been used for decades, and has been criticized for its uncertainty, inaccuracy, over-conservatism, high variability, and lack of transparency. A novel assessment factor method is proposed as an alternative to traditional AF methods. The proposed method offers novel features attempting to address these criticisms: it breaks the factor down into three components - endpoint standardization, extrapolation for species variation, and mode-of-action consideration (each of which has sub-components). This method also accommodates the inclusion of read-across and modeled data to fill data gaps for the substance. In addition, it better manages atypical datasets and atypical endpoints. Finally, and paramount to this endeavor, this novel method improves consistency among risk assessors, and can increase transparency of the entire PNEC derivation process to all stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".