Workgroup Report: Review of Fish Bioaccumulation Databases Used to Identify Persistent, Bioaccumulative, Toxic Substances
Bibliographic record
Abstract
Chemical management programs strive to protect human health and the environment by accurately identifying persistent, bioaccumulative, toxic substances and restricting their use in commerce. The advance of these programs is challenged by the reality that few empirical data are available for the tens of thousands of commercial substances that require evaluation. Therefore, most preliminary assessments rely on model predictions and data extrapolation. In November 2005, a workshop was held for experts from governments, industry, and academia to examine the availability and quality of in vivo fish bioconcentration and bioaccumulation data, and to propose steps to improve its prediction. The workshop focused on fish data because regulatory assessments predominantly focus on the bioconcentration of substances from water into fish, as measured using in vivo tests or predicted using computer models. In this article we review of the quantity, features, and public availability of bioconcentration, bioaccumulation, and biota-sediment accumulation data. The workshop revealed that there is significant overlap in the data contained within the various fish bioaccumulation data sources reviewed, and further, that no database contained all of the available fish bioaccumulation data. We believe that a majority of the available bioaccumulation data have been used in the development and testing of quantitative structure-activity relationships and computer models currently in use. Workshop recommendations included the publication of guidance on bioconcentration study quality, the combination of data from various sources to permit better access for modelers and assessors, and the review of chemical domains of existing models to identify areas for expansion.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".