PSXIII-12 Estimation of zinc response in terms of growth performance and plasma zinc in pigs by meta-analysis.
Bibliographic record
Abstract
Abstract Zinc is an essential nutrient for many metabolisms including protein, carbohydrate and lipid metabolism. Current EU legislation allows up to 150 ppm of total Zn/kg of feed, but the NRC estimate that 100 mg Zn/kg can fulfill the requirement. When pig is fed excess, a large part of this Zn is excreted causing environmental issues and participating in overall antibioresistance. The purpose of this study was to carry out a meta-analysis of the available literature information to determine the response different criteria to dietary Zn concentration. Plasma Zn, growth performance and bone data were search. Studies that used at least three concentrations of Zn in the diet were selected, this resulted in 65 Zn dose–response experiments. Then, only experiment where increasing Zn concentration add an impact on Y criteria were kept. Because of differences between experiments (e.g., age, genetic, duration) the response criteria were standardized with the highest response receiving a value of 100% and the other express in relative to this control. Also only experiment with doses lower than 1200 ppm were kept. This results in 13 dose-response for Average daily gain (ADG), 11 for Average Daily Feed Intake (ADFI), 24 for plasma zinc and only 6 for bone Zn which has not been studied due to a lack of data. The linear-plateau (LP) and quadratic-plateau (QP) models were tested to estimate the Zn requirement using ADG, ADFI and Plasma Zn. Due to scarcity of data, it was not possible to assess the impact of diet-related factors that influence the dietary Zn response such as phytate, calcium and copper. As expected, a Zn concentration below the requirement resulted in important reductions in both growth performance and plasma Zn. Model type has an important impact on the estimated requirement while the fit was similar. Using ADG and ADFI as response criteria, the Zn requirement was estimated at 58 ± 5.6 ppm for ADG with the LP and 80 ± 11 ppm with the QP. For ADFI the requirements were 52 ± 4.7 ppm and 64 ± 8.7 ppm for LP and QP respectively. Finally, for plasma Zn the requirements were higher than for growth performance; 86 ± 6.2 ppm and 114 ± 12 ppm. Using the LP model, increasing the Zn level from 20 to 40 ppm resulted in an increase in ADG, ADFI and plasma Zn by 21, 16 and 49%. Based on the outcome of this study, the requirements varied between 52 ppm for ADG with the QP model to 126 ppm for plasma Zn with the QP model. This latter value is higher than the 100-ppm recommendation of NRC (2012), but lower than the 150-ppm maximum level allowed for swine in Europe.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.060 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".