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ẢNH HƯỞNG CỦA VIỆC GIẢM PROTEIN THÔ TRONG KHẨU PHẦN TRÊN CƠ SỞ CÂN ĐỐI CÁC AXIT AMIN THIẾT YẾU DẠNG TIÊU HÓA HỒI TRÀNG TIÊU CHUẨN ĐẾN KHẢ NĂNG SINH TRƯỞNG VÀ CHẤT LƯỢNG THỊT CỦA GÀ LƯƠNG PHƯỢNG

2025· article· en· W4414213475 on OpenAlexaff
Huyền Ninh Thị, Đăng Phạm Kim, Ngọc Trần Thị Bích

Bibliographic record

VenueTạp chí Khoa học Nông nghiệp Việt Nam · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsFeed conversion ratioCompletely randomized designProtein dietProtein qualityDietary protein

Abstract

fetched live from OpenAlex

This study aimed to evaluate the effects of reduced crude protein (CP) level with supplementing standard ileal digestible essential amino acids (SID-EAA) on growth performance, feed efficiency and meat quality of Luong Phuong broilers. The experiment was a completely randomized design with 5 dietary treatments and 5 replicates. Total 750 Luong Phuong chickens were randomly located to one of 5 diets with reducing CP levels 1-4% compared with the standard CP diet and growth performance and meat quality were recorded accordingly. The results showed that reducing crude protein levels in the diet reduced the growth rate of chickens (P <0.05). Low protein diets did not affect the feed intake but increased feed coversation ratio (FCR) (P <0.05). However, at 1 and 2% protein reductions, the differences in both growth rate and feed conversion were not significant compared with the control group. Treatment 3 had the lowest feed cost (29,543 VND/kg), 539 VND/kg (1.792%) lower than the control group. Reducing CP at 4% (NT5) reduced breast meat ratio, increased 24h pH and increased breast meat toughness of chicken. In conclusion, the appropriate protein reduction level with SID-EAA supplement was less than 2% in the diet for Luong Phuong chicken.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.007
GPT teacher head0.209
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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