Limitations to Empirical Extrapolation Studies: The Case of BMD Ratios
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
Extrapolation relationships are of keen interest to chemical risk assessment in which they play a prominent role in translating experimentally derived (usually in animals) toxicity estimates into estimates more relevant to human populations. A standard approach for characterizing each extrapolation relies on ratios of pre-existing toxicity estimates. Applications of this "ratio approach" have overlooked several sources of error. This article examines the case of ratios of benchmark doses, trying to better understand their informativeness. The approach involves mathematically modeling the process by which the ratios are generated in practice. Both closed form and simulation-based models of this "data-generating process" (DGP) are developed, paying special attention to the influence of experimental design. The results show the potential for significant limits to informativeness, and revealing dependencies. Future applications of the ratio approach should take imprecision and bias into account. Bootstrap techniques are recommended for gauging imprecision, but more complicated techniques will be required for gauging bias (and capturing dependencies). Strategies for mitigating the errors are suggested.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| 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 it