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Record W2149821897 · doi:10.1111/asj.12398

Estimation of the optimum standardized ileal digestible total sulfur amino acid to lysine ratio in late finishing gilts fed low protein diets supplemented with crystalline amino acids

2015· article· en· W2149821897 on OpenAlexaff
Wenfeng Ma, Jinlong Zhu, Xiangfang Zeng, Xutong Liu, Philip Thacker, Shiyan Qiao

Bibliographic record

VenueAnimal Science Journal · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Saskatchewan
FundersNational Key Research and Development Program of China
KeywordsMethionineLysineFeed conversion ratioAnimal scienceQuadratic modelAmino acidChemistrySulfurBody weightBiochemistryBiologyChromatographyEndocrinology

Abstract

fetched live from OpenAlex

A total of 90 gilts were used to investigate the effects of various standard ileal digestible (SID) total sulfur amino acid (TSAA) to lysine (Lys) ratios on the performance and carcass characteristics of late finishing gilts receiving low crude protein (CP) diets supplemented with crystalline amino acids (CAA). Graded levels of crystalline methionine (Met) (0, 0.3, 0.5, 0.8 or 1.1 g/kg) were added to the basal diet to produce diets providing SID TSAA to Lys ratios of 0.48, 0.53, 0.58, 0.63 or 0.68. At the termination of the experiment, 30 gilts (one pig per pen) with an average body weight (BW) of 120 kg were killed to evaluate carcass traits. Increasing the SID TSAA to Lys ratio increased average daily gain (ADG) (linear and quadratic effect, P < 0.05), improved feed conversion ratio (FCR) (linear and quadratic effect, P < 0.05) and decreased serum urea nitrogen (SUN) concentration (linear and quadratic effect, P < 0.05) of finishing gilts. No effects were obtained for carcass traits. The optimum SID TSAA to Lys ratios to maximize ADG as well as to minimize FCR and SUN levels were 0.57, 0.58 and 0.53 using a linear-break point model and 0.64, 0.62 and 0.61 using a quadratic model.

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 score0.866
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.256
Teacher spread0.233 · 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
Published2015
Admission routes1
Has abstractyes

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