Risk-Based Prioritization of Organic Chemicals and Locations of Ecological Concern in Sediment From Great Lakes Tributaries
Bibliographic record
Abstract
With improved analytical techniques, environmental monitoring studies are increasingly able to report the occurrence of tens or hundreds of chemicals per site, making it difficult to identify the most relevant chemicals from a biological standpoint. For the present study, organic chemical occurrence was examined, individually and as mixtures, in the context of potential biological effects. Sediment was collected at 71 Great Lakes (USA/Canada) tributary sites and analyzed for 87 chemicals. Multiple risk-based lines of evidence were used to prioritize chemicals and locations, including comparing sediment concentrations and estimated porewater concentrations with established whole-organism benchmarks (i.e., sediment and water quality criteria and screening values) and with high-throughput toxicity screening data from the US Environmental Protection Agency's ToxCast database, estimating additive effects of chemical mixtures on common ToxCast endpoints, and estimating toxic equivalencies for mixtures of alkylphenols and polycyclic aromatic hydrocarbons (PAHs). This multiple-lines-of-evidence approach enabled the screening of more chemicals, mitigated the uncertainties of individual approaches, and strengthened common conclusions. Collectively, at least one benchmark/screening value was exceeded for 54 of the 87 chemicals, with exceedances observed at all 71 of the monitoring sites. Chemicals with the greatest potential for biological effects, both individually and as mixture components, were bisphenol A, 4-nonylphenol, indole, carbazole, and several PAHs. Potential adverse outcomes based on ToxCast gene targets and putative adverse outcome pathways relevant to individual chemicals and chemical mixtures included tumors, skewed sex ratios, reproductive dysfunction, hepatic steatosis, and early mortality, among others. The results provide a screening-level prioritization of chemicals with the greatest potential for adverse biological effects and an indication of sites where they are most likely to occur. Environ Toxicol Chem 2022;41:1016-1041. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".