Safety of Some Synthetic Food Colours: Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Food additives are used to protect food, increase quality and extend shelf life in many stages, from production to consumption of food. Colorants added by food producers to color food or to adjust the color to desired level are among the commonly used food additives. Considering today's developing production technologies, foods fade or discolor at various stages of processing, storage, and sale due to physical and chemical conditions such as heat, light, pH and oxygen. Colorants are used to regain these color losses, to enhance weak colors, to give color to the food that is actually colorless, and to win back the favour of customers by hiding low quality. Therefore, the most used food colorants in Canada, China, European Union, Mexico and United States of America were presented. The chemical name, chemical structure, common uses, acceptable daily intake (ADI), and toxicity literature of six most used artificial food colors were reviewed.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it