Estimating standardized ileal digestible valine requirements for broiler chickens based on two different meta-analytical selection procedures
Bibliographic record
Abstract
A low CP diet is an efficient strategy to decrease the environmental footprint of chicken farms, but this requires a precise knowledge of the requirements for essential amino acids ( AAs ). Many dose–response studies of valine have sought to estimate the standardized ileal digestible ( SID ) Valine ( Val ) requirement. Requirements vary because of many factors, such as differences in broiler ages, genetic strains, basal diet nutritional composition, and statistical model used. The purpose of this study was to estimate the Val requirement of broilers through a meta-analysis performed in two ways: first, based on amino acid requirements ( AminoAcidDB ); and second, based on the significant response ( ResponseDB ) of broilers to SID Val supply. The first database includes 8 papers describing 11 experiments and 63 treatments. The second database includes 17 papers describing 26 experiments and 144 treatments. The quadratic ( QD ) and curvilinear-plateau ( CLP ) models were tested to estimate the SID Val:Lysine ( Lys ) requirement using the average daily gain ( ADG ), average daily feed intake ( ADFI ), and gain-to-feed ratio ( G:F ) as response criteria. The ADFI did not converge with the CLP model in either database. When using the AminoAcidDB with the QD model, the estimated SID Val:Lys requirements (95% of the maximum) were ADG of 84.6%, ADFI of 76.7%, and G:F of 88.6%. With the CLP model, ADG was 82.4% and G:F was 87.1%. In the ResponseDB using the QD model, the SID Val:Lys requirements were 82.0% for ADG, 80.9% for ADFI, and 79.0% for G:F; the CLP model predicted 84.5% for ADG and 83.6% for G:F. This study also confirmed the presence of interactions between branched-chain AA and their impact on broiler performance, with Leu appearing to be the main regulator. In light of this meta-analysis, Val recommendations appear to be underestimated and should consider the influence of other AAs on the response. The results of this meta-analysis will facilitate the implementation of the low CP strategy without affecting broiler performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".