EURL ECVAM Genotoxicity and Carcinogenicity Database of Substances Eliciting Negative Results in the Ames Test: Construction of the Database
Bibliographic record
Abstract
The bacterial reverse mutation test (Ames test) is the most commonly used genotoxicity test; it is a primary component of the chemical safety assessment data required by regulatory agencies worldwide. Within the current accepted in vitro genotoxicity test battery, it is considered capable of revealing DNA reactivity, and identifying substances that can produce gene mutations via different mechanisms. The previously published consolidated EURL ECVAM Genotoxicity and Carcinogenicity Database, which includes substances that elicited a positive response in the Ames test, constitutes a collection of data that serves as a reference for a number of regulatory activities in the area of genotoxicity testing. Consequently, we considered it important to expand the database to include substances that fail to elicit a positive response in the Ames test, i.e., Ames negative substances. Here, we describe a curated collection of 211 Ames negative substances, with a summary of complementary data available for other genotoxicity endpoints in vitro and in vivo, plus available carcinogenicity data. A descriptive analysis of the data is presented. This includes a representation of the chemical space formed by the Ames-negative database with respect to other substances (e.g. REACH registered substances, approved drugs, pesticides, etc.) and a description of the organic functional groups found in the database. We also provide some suggestions on further analyses that could be made.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".