Estimation of the bioaccumulation potential of a nonchlorinated bisphenol and an ionogenic xanthene dye to <i>Eisenia andrei</i> in field-collected soils, in conjunction with predictive in silico profiling
Bibliographic record
Abstract
In silico-based model predictions, originating from structural and mechanistic (e.g., transport, bioavailability, reactivity, and binding potential) profiling, were compared against laboratory-derived data to estimate the bioaccumulation potential in earthworms of 2 organic substances (1 neutral, 1 ionogenic) known to primarily partition to soil. Two compounds representative of specific classes of chemicals were evaluated: a nonchlorinated bisphenol containing an -OH group (4,4′-methylenebis[2,6-di-tert-butylphenol] [Binox]), and an ionogenic xanthene dye (2′,4′,5′,7′-tetrabromo-4,5,6,7-tetrachloro-3′,6′-dihydroxy-, disodium salt [Phloxine B]). Soil bioaccumulation studies were conducted using Eisenia andrei and 2 field-collected soils (a clay loam and a sandy soil). In general, the in silico structural and mechanistic profiling was consistent with the observed soil bioaccumulation tests. Binox did not bioaccumulate to a significant extent in E. andrei in either soil type; however, Phloxine B not only accumulated within tissue, but was not depurated from the earthworms during the course of the elimination phase. Structural and mechanistic profiling demonstrated the binding and reactivity potential of Phloxine B; this would not be accounted for using traditional bioaccumulation metrics, which are founded on passive-based diffusion mechanisms. This illustrates the importance of profiling for reactive ionogenic substances; even limited bioavailability combined with reactivity can result in exposures to a hazardous substance not predictable by traditional in silico modeling methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".