FEATURES AND DIFFERENCES OF THE SYSTEM OF HAZARD CLASSIFICATION AND LABELING OF CHEMICAL PRODUCTS IN SAFETY DATA SHEET FOR VARIOUS COUNTRIES
Bibliographic record
Abstract
The article presents a study on the classification and labeling in accordance with the GHS when issuing safety data sheets for chemical products. The purpose of the study is to identify features and differences in chemical safety data sheets developed in accordance with the legislation of a number of countries, as well as to analyze the main errors in the preparation of SDS, and in particular in the section related to the GHS classification. In the article, on specific examples, the differences in the classification of chemical products are considered, as well as explanations of these differences within the framework of the legislation used by countries. As research methods, a comparison method was used to assess the differences between the existing legislations of countries and identify criteria for hazard types that have significant differences, as well as a method for analyzing existing national documents that control safety when working with chemical products. Using these methods, the main differences in the classification of products according to the GHS and the issuing of the SDS were presented in comparison with the criteria for classifying products under Russian legislation with the classification of other countries (European Union countries, USA, Canada, etc.).
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".