Biologically Motivated Computational Modeling: Contribution to Risk Assessment
Bibliographic record
Abstract
The article highlighted in this issue is “Human Respiratory Tract Cancer Risks of Inhaled Formaldehyde: Dose Response Predictions Derived from Biologically Motivated Computational Modeling of a Combined Rodent and Human Dataset,” by Rory Conolly, Julia Kimbell, Derek Janszen, Paul Schlosser, Darin Kalisak, Julian Preston, and Frederick Miller. In the featured article, Conolly et al. describe the development of the human component of a biologically motivated computational model to predict exposure response at levels of formaldehyde less than those associated with squamous cell carcinomas (SCC) observed in Fischer 344 rats exposed by inhalation. The article addresses extension of the computational model to the entire respiratory tract of humans, complementing a previous description, which presented modeling for the nasal airways of rats (Conolly et al., 2003). Extension to the entire respiratory tract is relevant for prediction of risk associated with oronasal breathing of humans, as occurs at higher exertion levels characteristic of those likely in the occupational environment.
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.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".