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Record W3205706179 · doi:10.3390/ani11102914

Modelling Broiler Requirements for Lysine and Arginine

2021· article· en· W3205706179 on OpenAlexaff
Bernardo Rocha Franco Nogueira, Nilva Kazue Sakomura, Matheus de Paula Reis, Bruno Balbino Leme, Marie-Pierre Létourneau-Montminy, Gabriel da Silva Viana

Bibliographic record

VenueAnimals · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversité Laval
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsBroilerLysineArginineBody weightAnimal scienceFactorial experimentAmino acidMethionineLinear regressionChemistryBiologyBiochemistryEndocrinologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Six assays were conducted to investigate male and female broiler responses to standardized ileal digestible (SID) lysine (Lys) and arginine (Arg). Response data were modeled to estimate the efficiency of utilization (k) of both amino acids and adjust factorial models to predict bird intake for SID Lys and SID Arg. In each assay, 1280 Cobb 500® broilers (640 male and 640 female) were randomly assigned to one of sixteen dietary treatments with four replicates of 20 birds. Dietary treatments consisted of crescent levels of SID Lys or SID Arg based on the dilution technique. The values of k determined for each phase (1- to 14-, 15- to 28-, and 29- to 42-d-old) and sex were contrasted using linear regression with groups (sex and phase). The estimated efficiencies were 0.79 for Lys and 0.62 for Arg, which were unaffected by phase or sex. Factorial models based on body weight and weight gain (M1) and on body and feather protein weight and deposition (M2) were applied to estimate the SID Lys and Arg intake for growth. The amino acid intake based on M2 had a lower error of prediction. Broiler chickens require 94.9 and 92.9 mg/d of SID Lys and SID Arg to deposit 1 g of body protein.

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 score0.950
Threshold uncertainty score0.385

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 head0.083
GPT teacher head0.269
Teacher spread0.186 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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