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Record W1865602325 · doi:10.3382/ps/pev280

A meta-analysis of the effects of nonphytate phosphorus on broiler performance and tibia ash concentration

2015· review· en· W1865602325 on OpenAlexaff
A. Faridi, A. Gitoee, J. France

Bibliographic record

VenuePoultry Science · 2015
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBroilerPhosphorusAnimal sciencePhytaseChemistryFeed conversion ratioCalciumNutrientFood scienceBiologyEndocrinologyBody weight

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.304
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations34
Published2015
Admission routes1
Has abstractyes

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