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Record W4313562057 · doi:10.1139/cjas-2022-0104

Effect of lupin (<i>Lupinus angustifolius</i>) as a soybean meal replacement on the performance, meat quality, and blood parameters of broilers

2023· article· en· W4313562057 on OpenAlexvenueno aff
Chun Ik Lim, Nag Jin Choi

Bibliographic record

VenueCanadian Journal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsSoybean mealFeed conversion ratioLupinus angustifoliusMealAnimal scienceFood sciencePolyunsaturated fatty acidBiologyBody weightFatty acidAgronomyBiochemistry

Abstract

fetched live from OpenAlex

This study investigated the effects of dietary lupin (LP) as a replacement for soybean meal (SBM) on the performance, meat quality, and blood parameters of broilers. A total of 960 1-day-old Ross 708 broilers were divided into four dietary groups. The four diets were formulated with different levels of dehulled LP content in place of SBM; LP0: 0, LP50: 50, LP100: 100, and LP200: 200 g/kg. There was a trend ( P &lt; 0.10) for reduced weight gain in the LP200 group compared with other groups. The feed conversion ratio was higher ( P &lt; 0.05) in the LP200 group than in the LP0 and LP50 groups. Concerning breast meat characteristics, the lightness color (L * ) was lower ( P &lt; 0.05) in the LP200-fed group compared with the LP0 group. Polyunsaturated fatty acids were higher ( P &lt; 0.05) in LP100- and LP200-fed chickens than in LP0-fed chickens. Serum HDL cholesterol was significantly ( P &lt; 0.05) higher in the group fed LP200 compared with the groups fed LP0 and LP50. A higher serum concentration of interleukin (IL)-2 was found in groups fed LP100 and LP200 than in groups fed LP0 and LP50. Our results suggest that LP could be a dose-dependent SBM substitute, and that the optimal level of LP inclusion is approximately 100 g/kg.

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)0.002
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.282
Teacher spread0.249 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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