Bibliographic record
Abstract
The relevant acts,regulations and executive department and evaluation frameworks for new chemicals management in the EU,United States,Japan,Canada and Australia were introduced and summarized in this paper.The health effect data needs and their test methods for chemicals declaration were expounded in detail.The test data of ocular/dermal irritation/corrosion,skin sensitization,acute toxicity and genetic/chromosomal toxicity are elemental requirements in the EU,Canada and Australia.Japan pays much attention on new chemicals test data of persistence,bioaccumulation,and human/environmental toxicity rather than acute toxicity.In the United States,the predicted health effect data by SARs/QSARs are essential;if it is difficult to make a conclusion whether a chemical poses potential risk of injury to human health/environment,testing data is required.On the contrary,QSARs method is accepted with discretion in Australia.Finally,the management regulation of new chemicals in China was presented briefly.In view of other countries' experience,some complementary proposals about the registered new chemicals data base,QSARs,PET assessment and GLP laboratory on the health effect data needs and their management were presented.
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 | 0.061 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".