Exposure‐Based Validation List for Developmental Toxicity Screening Assays
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
Validation of alternative assays requires comparison of the responses to toxicants in the alternative assay with in vivo responses. Chemicals have been classified as "positive" or "negative" in vivo, despite the fact that developmental toxicity is conditional on magnitude of exposure. We developed a list of positive and negative developmental exposures, with exposure defined by toxicokinetic data, specifically maternal plasma Cmax . We selected a series of 20 chemicals that caused developmental toxicity and for which there were appropriate toxicokinetic data. Where possible, we used the same chemical for both positive and negative exposures, the positive being the Cmax at a dose level that produced significant teratogenicity or embryolethality, the negative being the Cmax at a dose level not causing developmental toxicity. It was not possible to find toxicokinetic data at the no-effect level for all positive compounds, and the negative exposure list contains Cmax values for some compounds that do not have developmental toxicity up to the highest dose level tested. This exposure-based reference list represents a fundamentally different approach to the evaluation of alternative tests and is proposed as a step toward application of alternative tests in quantitative risk assessment.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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