Growing–finishing pigs do not need additional zinc in a phytase-supplemented wheat–barley–soybean meal-based diet
Bibliographic record
Abstract
Focus remains on reducing the excretion of surplus zinc (Zn) from pigs through manure due to environmental and public health concerns. Growing–finishing pigs may need less dietary Zn than current EU legislation allows and what is typically applied on farms. The aim of this study was to evaluate the effect of three dietary Zn levels on productivity, Zn homeostasis, and health status in growing pigs fed a grain and soybean meal-based diet with a high inclusion of phytase (1000 phytase units). Ninety pigs were offered a diet with 1431 and 83 ppm total Zn from weeks 0–2 and 2–6 post-weaning, respectively, followed by one of three dietary Zn levels (31 (unsupplemented), 69, or 102 ppm total Zn, n = 30/Zn level) from weeks 6–16 post-weaning (30–110 kg). Productivity and health were unaffected by dietary Zn level. Despite differences in serum Zn according to dietary Zn level in week 10, serum Zn remained above the assumed sufficient level following all dietary Zn levels through the experiment. No signs of parakeratosis were observed, and we conclude that growing–finishing pigs produce and stay healthy without added Zn to a phytase supplemented grain–soybean meal-based diet when optimal dietary Zn levels are applied up to 30 kg.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".