Bibliographic record
Abstract
Abstract Hair products are normally cosmetics and are, thus, subject to all laws and regulations that control the labeling and claims of all cosmetic products. There are some variations, namely hair colorants, professional use products, and products that make drug claims. Shampoo is the largest segment of the market. The primary purpose of shampoo is to clean hair and scalp from various soils. Synthetic detergents are the backbone of shampoos. The manufacture of shampoo is quite easy requiring only a suitable kettle, heating and cooling capabilities, and a mixer. Hair conditioners are rinses, hair dressing, and intensive treatments. Conditioners provide different effects to the hair and make combing easier. Finishing sprays and fixatives help style the hair. Colorants are divided into temporary, semipermanent and permanent systems. Toxicological data is of primary importance. In the U.S. the classification of color additives is complex. Waving and straightening products are also discussed. Cosmetic products in the United States are regulated by the FDA. If a cosmetic is intended to treat a disease or affect the function of the body, it is considered a drug.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.179 | 0.087 |
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".