Bioaccumulation Assessment Using Predictive Approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mandated efforts to assess chemicals for their potential to bioaccumulate within the environment are increasingly moving into the realm of data inadequacy. Consequently, there is an increasing reliance on predictive tools to complete regulatory requirements in a timely and cost-effective manner. The kinetic processes of absorption, distribution, metabolism, and elimination (ADME) determine the extent to which chemicals accumulate in fish and other biota. Current mathematical models of bioaccumulation implicitly or explicitly consider these ADME processes, but there is a lack of data needed to specify critical model input parameters. This is particularly true for compounds that are metabolized, exhibit restricted diffusion across biological membranes, or do not partition simply to tissue lipid. Here we discuss the potential of in vitro test systems to provide needed data for bioaccumulation modeling efforts. Recent studies demonstrate the utility of these systems and provide a "proof of concept" for the prediction models. Computational methods that predict ADME processes from an evaluation of chemical structure are also described. Most regulatory agencies perform bioaccumulation assessments using a weight-of-evidence approach. A strategy is presented for incorporating predictive methods into this approach. To implement this strategy it is important to understand the "domain of applicability" of both in vitro and structure-based approaches, and the context in which they are applied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 it