Vitamin A and Carotenoids
Bibliographic record
Abstract
Vitamin A has an important role to play in vision, bone growth, reproduction, cell division, and cell differentiation. With the focus on Vitamin A and Carotenoids, this book includes the latest research in these areas and starts with an overview putting the compounds in context with other vitamins, supplementation and discussing the importance of beta-carotene. Details of the chemistry, structure and biochemistry of the compounds begins with nomenclature followed by information on encapsulation, thermal degradation and occurrence. Developments in analytical and bioanalytical techniques concerning these compounds in plant, milk and human tissue systems are covered in detail. Finally, the book covers the extensive functions and effects of Vitamin A on eg developmental growth, immune function, cancer risk, the brain and lungs as well as vision. Delivering high quality information, this book will be of benefit to anyone researching this area of health and nutritional science. It will bridge scientific disciplines so that the information is more meaningful and applicable to health in general. Part of a series of books, it is specifically designed for chemists, analytical scientists, forensic scientists, food scientists, dieticians and health care workers, nutritionists, toxicologists and research academics. Due to its interdisciplinary nature it could also be suitable for lecturers and teachers in food and nutritional sciences and as a college or university library reference guide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.020 |
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".