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Record W2460779065 · doi:10.17221/8722-vetmed

Effects of lactulose on growth, carcass characteristics, faecal microbiota, and blood constituents in broilers

2016· article· en· W2460779065 on OpenAlexaff
Mohsen Mohammadi Gheisar, C. M. Nyachoti, Joe D Hancock, In Ho Kim

Bibliographic record

VenueVeterinární Medicína · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLactuloseBroilerAnimal scienceBody weightMealFeed conversion ratioFood scienceBiologyLactobacillusFermentationBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

This study was conducted to determine the effect of supplementing diets with lactulose on growth performance, carcass characteristics, faecal microbiota, and blood constituents. A total of 324 one-day-old Ross 308 mixed-sex broiler chicks with an average initial body weight of 38 g were used in a 35-day growth assay. There were 18 birds/pen and six pens/treatment with food and water available ad libitum. Treatments consisted of a corn-soybean-meal-based diet with 0, 0.25 and 0.5% of lactulose. The results indicated that body weight gain (BWG) was improved (linear effect, P < 0.05) by increasing the concentration of lactulose in the diet from zero to 0.5% while the feed conversion ratio (FCR) decreased (linear effect, P < 0.05) for Days 8 to 21, 21 to 35, and overall (Day 0 to 35). Chickens fed the diet supplemented with 0.5% lactulose showed a higher relative weight of breast meat compared to other groups. Inclusion of lactulose decreased the count of Salmonella and E. coli in excreta of chickens fed diets containing 0.25 or 0.5% lactulose (P < 0.05), but the count of Lactobacillus was not affected. Drip loss percentage was decreased (P < 0.05) on Day 1 by addition of 0.5% lactulose, but there was no effect on meat colour. Blood characteristics were not influenced. Thus, it was concluded that inclusion of lactulose improves growth performance and alters excreta microbial populations with no adverse effect on broilers.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.185
Teacher spread0.178 · 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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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