Scientific substantiation of the national list of chemicals affecting the endocrine system
Bibliographic record
Abstract
Introduction. Today in the world, along with mutagens, carcinogens, reprotoxicants, chemicals that affect the endocrine system are of serious concern. Therefore, the purpose of our research was the scientific substantiation of the national list of endocrine disruptors. Materials and methods. In order to create a national list of substances that have an effect on the endocrine system, for the selection of candidate substances, an analysis of legislation and lists of potential endocrine disruptors of the European Union, the USA, Canada, and India was made. All substances were identified by CAS numbers, areas of application and classified according to the degree of danger to the endocrine system. Results. The draft list of endocrine disruptors circulating in the Russian Federation includes 494 chemicals used in medicine, agriculture, chemical, food, perfume and cosmetic industries. There are 19 substances in class 1A (known endocrine disruptor), 193 substances in class 1B (probable endocrine disruptor), 250 substances in class 2 (suspected endocrine disruptors), and 32 substances in class 3 (endocrine disruptors). Conclusion. On the territory of the Russian Federation in various areas of the economy, chemicals that destroy the endocrine system are widely used. Of particular concern is the use of endocrine disruptors of hazard class 1B in perfumery, cosmetics and food products.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".