Vitamin nutrition of livestock animals: Overview from vitamin discovery to today
Bibliographic record
Abstract
The term “vitamin” or “vitamine” was first used in 1912. What later became known as vitamin deficiency diseases — scurvy, beriberi, night blindness and xeropthalmia — had plagued the world from antiquity. From 1900 through the 1930s, experiments with animals helped to advance knowledge of vitamins considerably. There are 15 vitamins of significance for livestock. A number of factors influence vitamin requirements and vitamin utilization, including physiological make-up and production function; confinement rearing without pasture; stress, disease and adverse environmental conditions; vitamin antagonists; use of antimicrobial drugs; and body vitamin reserves. Under commercial livestock and poultry production conditions, vitamin allowances higher than National Research Council (USA) requirements may be needed for optimum performance. Generally, the optimum vitamin supplementation level is the quantity that achieves the best growth rate, feed utilization and health (including immune competency), while also providing adequate body reserves. Key words: Vitamins, history, deficiency, requirements, supplementation
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".