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Record W2965757818 · doi:10.1093/jas/skz122.042

46 How far we could go reducing crude protein with the use of supplemental amino acids

2019· article· en· W2965757818 on OpenAlexaff
C. Pomar

Bibliographic record

VenueJournal of Animal Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsNutrientExcretionAnimal scienceBiologyPopulationAmino acidMealEssential nutrientEnergy requirementFood scienceBiochemistryMedicineMathematics

Abstract

fetched live from OpenAlex

Abstract Feeding growing pigs with diets providing the required amount of essential and non-essential amino acids (AA) reduces energy expenditure and minimizes N excretion. Low protein diets can be obtained by supplementing feeds with crystalline AA. Numerous experiments have evaluated the ideal dietary AA concentration at different growth stages, but reducing dietary protein with the use of supplemental AA is limited by the inaccuracy of the principles used to estimate AA requirements. One of these principles states that growing animals need AA for maintenance and growth. Maintenance requirements are related to BW whereas the efficiency of AA utilization (e.g., 72% for Lys) and body protein AA composition are constant (e.g., 7% for Lys). These parameters are, however, affected by AA restriction, meal frequency, energy supply, genetics, etc. Even when controlling these factors, individual pigs respond differently to the same AA supply. Yet pigs are raised in groups and fed with a unique feed for long periods. Individual pigs within a given population differ in terms of BW, ADG, health status, etc., and consequently, differ in the amount of AA they need at a given time. Therefore, when feeding a group of pigs, the concept of maintenance and growth requirements may not be appropriate. In this situation, nutrient requirements should be seen as the optimal balance between the proportion of animals that needs to be overfed and underfed. Given that for most AA, underfed animals exhibit reduced performance, whereas overfed animals exhibit near-optimal performance, optimal growth is obtained when nutrients are provided to satisfy the requirements of the most demanding animals. There is therefore a trade-off between performance and dietary protein reduction. The inaccuracy of the principles used to estimate AA requirements, both for individual animals and populations, limits how far we can go reducing dietary protein with the use of supplemental AA.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0140.005

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 head0.045
GPT teacher head0.248
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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