A meta-analysis of the effects of nonphytate phosphorus on broiler performance and tibia ash concentration
Bibliographic record
Abstract
Decreasing feed costs while maintaining broiler performance at a high level with minimal environmental pollution has become a major challenge for poultry nutritionists in recent years. In this regard, phosphorus (P) is a nutrient that is problematic. To overcome this, a comprehensive knowledge of the responses of broilers to P is needed and the factors that affect its utilization need better understanding. For this purpose, a meta-analysis was conducted using results published in the literature on the responses of broilers to different levels of nonphytate P (NPP), calcium (Ca), microbial phytase (MP), and vitamin D3 or its metabolites (VD). The effects of Ca, MP, and VD on NPP requirements were investigated. Results showed significant (P ≤ 0.0001) linear and quadratic effects of NPP on all the responses, viz. average daily gain (ADG), feed intake (FI), feed efficiency (FE), and tibia ash concentration (TA). Results showed the negative effect of high Ca levels on all investigated responses, although these deleterious effects were alleviated when levels of NPP were increased or MP and/or VD added. Synergistic effects of MP and VD on FI and TA were observed. Best performance for all responses was found when MP and VD were added to low or moderate levels of Ca and NPP. Optimization showed higher levels of NPP are required to maximize TA compared to ADG, FI, and FE. Based on our analysis, requirements for NPP were affected mostly by Ca (increased) and MP (decreased), and, to a lesser extent, VD (inconsistent).
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.057 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".