Estimation of digestible zinc and copper in pigs: a meta-analysis approach
Bibliographic record
Abstract
The objective of this study was to quantify the impact of Zn and Cu supplementation and nutritional factors on the digestible content of these minerals using a meta-analysis approach. A database derived from 24 publications and describing 142 experimental treatments was compiled. A model showed that positive effects of chelated, (linear and quadratic, P < 0.01) and inorganic Zn supplementation ( P < 0.05) (linear and quadratic, P < 0.01), Zn from ingredients (linear, P < 0.01) and a negative linear effect of dietary Cu concentration ( P = 0.067; R2= 94%) on digestible Zn content. In other model, addition of phytase showed linear improvement of digestible Zn ( P < 0.01; R2= 96%). For digestible Cu, a model showed that chelated and inorganic Cu supplementation ( P < 0.01) linearly increased digestible Cu while dietary Zn level decreased it ( P < 0.01; R2 = 93%). Dietary phytase did not impact digestible content of Cu. The digestibility of Zn and Cu depends on dietary supplementation of these minerals in diets but also on the interaction between these minerals. Finally, phytase supplementation improves digestible Zn content but not Cu.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".