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Record W4307117536 · doi:10.3390/ani12202877

Formulating Diets for Improved Health Status of Pigs: Current Knowledge and Perspectives

2022· review· en· W4307117536 on OpenAlexaff
Lucas Alves Rodrigues, Bonjin Koo, Martin Nyachoti, Daniel A Columbus

Bibliographic record

VenueAnimals · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of ManitobaGenome PrairieUniversity of Saskatchewan
Fundersnot available
KeywordsImmune systemContext (archaeology)NutrientBiologyMetabolismBiotechnologyImmunologyBiochemistryEcology

Abstract

fetched live from OpenAlex

Our understanding of nutrition has been evolving to support both performance and immune status of pigs, particularly in disease-challenged animals which experience repartitioning of nutrients from growth towards the immune response. In this sense, it is critical to understand how stress may impact nutrient metabolism and the effects of nutritional interventions able to modulate organ (e.g., gastrointestinal tract) functionality and health. This will be pivotal in the development of effective diet formulation strategies in the context of improved animal performance and health. Therefore, this review will address qualitative and quantitative effects of immune system stimulation on voluntary feed intake and growth performance measurements in pigs. Due to the known repartitioning of nutrients, the effects of stimulating the immune system on nutrient requirements, stratified according to different challenge models, will be explored. Finally, different nutritional strategies (i.e., low protein, amino acid-supplemented diets; functional amino acid supplementation; dietary fiber level and source; diet complexity; organic acids; plant secondary metabolites) will be presented and discussed in the context of their possible role in enhancing the immune response and animal performance.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.213
GPT teacher head0.402
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2022
Admission routes1
Has abstractyes

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