International Water Quality Guidelines for Polycyclic Aromatic Hydrocarbons: Advances to Improve Jurisdictional Uptake of Guidelines Derived Using The Target Lipid Model
Bibliographic record
Abstract
A large number of different of polycyclic aromatic hydrocarbons (PAHs) have been found in environmental media, yet water quality guidelines (WQGs) are only available for a small subset of PAHs, limiting our ability to adequately assess environmental risks from these compounds. The target lipid model (TLM) was published over 20 years ago and has been extensively validated in the literature, but it has still not been widely adopted by jurisdictions to derive WQGs for PAHs. The goal of our study was to better align the methods for deriving TLM-based WQGs with international derivation protocols. This included updating the TLM with rescreened data to identify datapoints by which effect concentrations were estimated rather than measured, modernizing the statistics used to generate the hazard concentration, and testing the applicability of a chronic TLM model rather than using the acute-to-chronic ratio. The results show that the acute TLM model did not deviate substantially from the previous iteration, indicating that the model has reached a point of stability after over 20 years of testing and improvements. Water quality guidelines derived directly from a chronic TLM provided a similar level of protection as previous iterations of the TLM. The major advantage of adopting TLM-derived WQGs is the expanded list of PAH WQGs, which will allow a more fulsome quantification of environmental risks and the ability to apply the model to mixtures. Environ Toxicol Chem 2024;43:686-700. © 2023 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 | 0.074 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".