Bioaccumulation data from laboratory and field studies: Are they comparable?
Bibliographic record
Abstract
Once they are released into the environment, a number of chemicals are known to bioaccumulate in organisms, sometimes to concentrations that may threaten the individual or their predators. However, use of physical or chemical properties or results from laboratory bioaccumulation tests to predict concentrations sometimes found in wild organisms remains a challenge. How well laboratory studies and field measurements agree or disagree, and the cause of any discrepancies, is a subject of great interest and discussion from both a scientific and a regulatory perspective. A workshop sponsored by the ILSI Health and Environmental Sciences Institute, US Environmental Protection Agency, and the Society of Environmental Toxicology and Chemistry assembled scientists from academia, industry, and government to compare and contrast laboratory and field bioaccumulation data. The results of this workshop are summarized in a series of 5 articles published in this issue of Integrated Environmental Assessment and Management. The articles describe: 1) a weight-of-evidence approach that uses fugacity ratios to bring field measurements into the assessment of biomagnification potential for legacy chemicals; 2) a detailed comparison between laboratory and field data for the most commonly measured bioaccumulation endpoint, the biota-sediment accumulation factor; 3) a study that identifies and quantifies the differences between laboratory and field metrics of bioaccumulation for aquatic and terrestrial organisms; and 4) 2 reports on trophic magnification factors: the 1st addresses how trophic magnification factors are determined and interpreted and the 2nd describes how they could be used in regulatory assessments. Collectively, these articles present the workshop participants' current understanding and assessment of bioaccumulation science and make a number of recommendations on how to improve the collection and interpretation of bioaccumulation data.
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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 | 0.198 | 0.451 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".