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Record W3197637880 · doi:10.3390/ani11092598

Toward Precise Nutrient Value of Feed in Growing Pigs: Effect of Meal Size, Frequency and Dietary Fibre on Nutrient Utilisation

2021· review· en· W3197637880 on OpenAlexaff
Élisabeth Chassé, Frédéric Guay, Knud Erik Bach Knudsen, R. T. Zijlstra, Marie-Pierre Létourneau-Montminy

Bibliographic record

VenueAnimals · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of AlbertaUniversité Laval
Fundersnot available
KeywordsNutrientMealFood scienceDigestion (alchemy)BiologyDietary fibreNutrient densityAnimal scienceChemistry

Abstract

fetched live from OpenAlex

Nutritional values of ingredients have been and still are the subject of many studies to reduce security margins of nutrients when formulating diets to reduce feed cost. In most studies, pigs are fed a limited amount of feed in a limited number of meals that do not represent how pigs are fed in commercial farm conditions. With free access to feed, pigs follow their intrinsic feeding behaviour. Feed intake is regulated by satiety and satiation signals. Reducing the feed intake level or feeding frequency can affect digestibility and transit time and induce metabolic changes. To reduce feed costs, alternative ingredients that are frequently rich in dietary fibre are added to diets. Fibre acts on the digestion process and transit time by decreasing energy density and causing viscosity. Various analyses of fibre can be realised, and the measured fibre fraction can vary. Exogenous enzymes can be added to counteract the effect of fibre, but digestive tract conditions, influenced by meal size and frequency, can affect the efficiency of supplemented enzymes. In conclusion, the frequency and size of the meals can affect the digestibility of nutrients by modulating gastrointestinal tract conditions (pH and transit time), metabolites (glucose and short-chain fatty acids) and hormones (glucagon-like peptide 1 and peptide tyrosine tyrosine).

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.055
GPT teacher head0.298
Teacher spread0.243 · 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 designOther design
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

Citations25
Published2021
Admission routes1
Has abstractyes

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