Developing tools for defining and establishing pathways of toxicity
Bibliographic record
Abstract
New approaches for toxicity testing: The US National Research Council report on ‘Toxicity Testing in the 21st Century’ (Krewski et al. 2010) envisioned a shift in testing away from studies of apical endpoints in test animals to the use of human cells to assess perturbations of toxicity pathways (TPs). The report generated widespread interest and has produced subsequent discussions regarding implementation of its key recommendations (Andersen and Krewski 2010; Krewski et al. 2011, 2014). TPs were defined as normal cellular signaling pathways that could serve as targets of toxicity in the face of perturbations of their function by chemical exposures. The examples provided in the report included sex steroid hormone receptor pathways, liver nuclear receptor signaling, and the suite of eight canonical stress pathways, including oxidative stress, DNA damage, heat shock, hypoxia, metal stress, inflammation, endoplasmic reticulum stress, and oxidative stress (Simmons et al. 2009). This aggregation of pathways, based largely on preexisting biological information, remains coarse-grained with many possible nodes in each of these TPs whose alterations could lead to toxicity. Some of the continuing challenges in advancing new, cell-based methods for toxicity testing are (1) the manner in which testing will be accomplished, (2) the degree of detail required to define the biological targets whose alterations lead to toxicity, and (3) the biological granularity underpinning definitions of toxicity pathways.
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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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.019 | 0.044 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".