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Record W2126826831 · doi:10.1093/ps/80.2.195

Effects of Feeding Locally Grown Whole Barley With or Without Enzyme Addition and Whole Wheat on Broiler Performance and Carcass Traits

2001· article· en· W2126826831 on OpenAlexafffund
J. Nahas, M.R. Lefrançois

Bibliographic record

VenuePoultry Science · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversité Laval
FundersUniversité Laval
KeywordsGizzardBroilerAbdominal fatAnimal scienceMealBiologyEnzymeFeed conversion ratioSoybean mealFood scienceBody weightBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

Two experiments were conducted to investigate the impact of increasing dietary levels of whole barley (WB) with or without exogenous enzymes and of whole wheat (WW) without E fed from 7 d of age, on performance and carcass characteristics of broilers. Experiment 1 was conducted with corn-soybean meal grower diets containing WB at 0, 10, 10 + enzymes, 15, or 15% + enzymes. The finisher diets contained, as fed, WB at 0, 15, 20 + enzymes, 15, or 20% + enzymes. In Experiment 2, grower diets contained 0, 10, 10, 20, or 20% WW with 0, 20, 35, 20, or 35% WW in the finisher diets. No enzymes were used for WW diets. In each Experiment, 1,500 1-d-old Ross x Ross male broilers were randomly distributed in 30 floor pens of 50 birds each. Six replicates were allotted to each treatment. Body weight, average daily gain (ADG), feed intake (FI), and feed efficiency ratio (FER) were measured at 7, 21, and at 38 d of age. In Experiment 1, ADG was lower (P < 0.05) in the control vs. Diet 5. However, FER with enzyme addition was lower, and FI with enzymes was higher (P < 0.05). Final BW, gizzard, and pancreas weights were higher (P < 0.05) with WB inclusion. In Experiment 2, ADG and BW significantly increased with addition of WW, although the response was best for Diets 2 and 3. Abdominal fat and carcass weights increased (P < 0.05) with the WW levels in the diets.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.216

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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 designObservational
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

Citations63
Published2001
Admission routes2
Has abstractyes

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