Comparing laboratory and field measured bioaccumulation endpoints
Bibliographic record
Abstract
An approach for comparing laboratory and field measures of bioaccumulation is presented to facilitate the interpretation of different sources of bioaccumulation data. Differences in numerical scales and units are eliminated by converting the data to dimensionless fugacity (or concentration-normalized) ratios. The approach expresses bioaccumulation metrics in terms of the equilibrium status of the chemical, with respect to a reference phase. When the fugacity ratios of the bioaccumulation metrics are plotted, the degree of variability within and across metrics is easily visualized for a given chemical because their numerical scales are the same for all endpoints. Fugacity ratios greater than 1 indicate an increase in chemical thermodynamic activity in organisms with respect to a reference phase (e.g., biomagnification). Fugacity ratios less than 1 indicate a decrease in chemical thermodynamic activity in organisms with respect to a reference phase (e.g., biodilution). This method provides a holistic, weight-of-evidence approach for assessing the biomagnification potential of individual chemicals because bioconcentration factors, bioaccumulation factors, biota-sediment accumulation factors, biomagnification factors, biota-suspended solids accumulation factors, and trophic magnification factors can be included in the evaluation. The approach is illustrated using a total 2393 measured data points from 171 reports, for 15 nonionic organic chemicals that were selected based on data availability, a range of physicochemical partitioning properties, and biotransformation rates. Laboratory and field fugacity ratios derived from the various bioaccumulation metrics were generally consistent in categorizing substances with respect to either an increased or decreased thermodynamic status in biota, i.e., biomagnification or biodilution, respectively. The proposed comparative bioaccumulation endpoint assessment method could therefore be considered for decision making in a chemicals management context.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".