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Record W4399293276 · doi:10.3390/ani14111667

Feeding a Novel Mannan-Rich Yeast Carbohydrate Product Improves Production Performance and Humoral Immunity of Broiler Chickens

2024· article· en· W4399293276 on OpenAlexaff
Anhao Wang, Anangelina Archile, Robert Patterson

Bibliographic record

VenueAnimals · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsCanadian Bio-Systems (Canada)
Fundersnot available
KeywordsMannanBroilerBiologyAnimal scienceFeed conversion ratioAntibody responseYeastImmune systemAntibodyFood scienceBody weightImmunologyEndocrinologyBiochemistryPolysaccharide

Abstract

fetched live from OpenAlex

The current study examined the benefits of a novel mannan-rich yeast carbohydrate product (YM) on broiler chicken growth performance and immune response against sheep red blood cells (SRBCs). A total of 144 newly hatched male Cornish cross broiler chicks were randomly assigned to four treatments with 12 cages per treatment and three birds per cage. The treatments were (1) control, basal diet; (2) YCW, basal diet + 1 g/kg yeast cell wall; (3) YM1, basal diet + 0.5 g/kg of a novel yeast mannan-rich product (YM); and (4) YM2, basal diet + 1 g/kg YM. Growth performance was measured at 14, 28, and 35 days of age (d). At 26 and 27 d, nine birds per treatment were immunized intravenously with SRBCs, and antibody responses against SRBCs were analyzed through a hemagglutination assay 7 days post-inoculation. Supplementing YM tended to improve broiler chicken weight gain from 29 to 35 d (p = 0.053). An improvement in the feed conversion ratio (FCR) was observed in the birds fed YM diets during 29–35 d and over the entire experimental period (0–35 d; p < 0.05). Furthermore, birds fed YM2 diets had more robust antibody responses against SRBCs than the control birds (p = 0.033). In conclusion, dietary supplementation of YM improved broiler chicken growth performance and antibody response against SRBCs.

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 score0.889
Threshold uncertainty score0.183

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 head0.027
GPT teacher head0.233
Teacher spread0.206 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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