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Record W2126630786 · doi:10.3382/japr.2011-00470

Dilution of broiler chicken diets with whole hulless barley

2012· article· en· W2126630786 on OpenAlexafffund
D. M. Anderson, Janice MacIsaac, A R Safamehr

Bibliographic record

VenueThe Journal of Applied Poultry Research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsNova Scotia Department of Agriculture
FundersDepartment of Agriculture, Nova Scotia
KeywordsBroilerFood scienceStarterDilutionBiologyChemistryAnimal science

Abstract

fetched live from OpenAlex

An experiment was conducted to determine the effects of diluting broiler diets with increasing levels of whole hulless barley (WHB) on growth performance and nutrient digestibility. A total of 144 one-day-old male broiler chicks were fed the same starter diet with 5% WHB dilution. Grower diets (16 to 24 d) were diluted with 0, 7.5, 15, and 22.5% WHB, and finisher diets (25 to 36 d) were diluted with 0, 15, 30, and 45% WHB, respectively. All diets were supplemented with a commercial dietary enzyme. Birds fed the 22.5 and 45% WHB in grower and finisher diets had lower 36-d BW (P ≤ 0.05) than those fed the other treatments. From 25 to 36 d, birds fed the 30 and 45% WHB diets were less efficient (P ≤ 0.05) than those fed the undiluted diet. The AME value of the 22.5% WHB grower diet was lower (P ≤ 0.05) than those of the other grower diets, and the AME value of the 15% WHB grower diet was significantly higher (P ≤ 0.05) than that of the undiluted diet. All WHB finisher diets had lower (P ≤ 0.05) CP contents than the undiluted diet. The AME values of the finisher diets diluted with WHB were lower (P ≤ 0.05) than that of the undiluted diet. On the basis of these growth performance and digestibility data, optimal growth performance may be achieved by diluting broiler chicken diets with WHB up to a level of 7.5% in the grower period and 15% in the finisher period.

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)0.001
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 score0.951
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.071
GPT teacher head0.306
Teacher spread0.235 · 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

Citations7
Published2012
Admission routes2
Has abstractyes

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