Modeling mode of action of industrial chemicals: Application using chemicals on Canada's Domestic Substances List (DSL)
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
Abstract Traditionally quantitative structure activity relationships (QSARs) are derived on the assumption that similar chemicals should behave in a toxicologically similar manner. The development of mechanistic models for acute toxicity requires the chemical classification to be based on similar mode of toxic action. Currently, the classification approaches used to predict the mode of toxic action are predominantly based on chemical fragments and, to a lesser extent, on their electronic properties. The discrete nature of the fragment‐based approach, however, yields a classification that does not consider the electronic features of the entire chemical that has “continuous character”. The present study is based on the assumption that chemicals with the same mode of toxic action should possess commonality in their steric and electronic structure. The COmmon REactivity PAtterns (COREPA) approach has been applied to define the global physical properties and quantum‐chemical descriptors best clustering chemicals according to their behavioural mode of action (MOA). The derived COREPA models for each mode were translated into decision trees and applied to screen the organic chemicals on Canada's Domestic Substances List (DSL) for their mode of toxic action.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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