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A 2‐fold increase in threonine intake is required in late pregnancy

2010· article· en· W2295969380 on OpenAlexaff
Crystal L Levesque, Soenke Moehn, Paul B. Pencharz, Ronald O. Ball

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersAjinomoto Pharmaceuticals
KeywordsPregnancyConceptusLitterPhenylalanineEarly pregnancy factorMedicineAnimal scienceAmino acidInternal medicineEndocrinologyGestationChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

Current recommendations for amino acid (AA) intake during pregnancy in humans and pigs are for a single intake throughout. This is illogical because requirement should be higher in late pregnancy due to dramatic increases in conceptus growth during the 3 rd trimester. THR requirement was determined using the indicator amino acid oxidation method in eight multiparous sows using L[1‐ 13 C]phenylalanine. Sows received diets ranging from 20 to 180% of the current recommended THR intake (10 gd −1 ) based on BW, expected pregnancy weight gain and litter size. Tracer phenylalanine was given orally in 8 ½‐hourly meals and expired 13 CO 2 was quantified. Data was analyzed using a nonlinear Mixed model. Sow reproductive performance was similar to commercial standards. The THR requirement in early pregnancy was 4.9 gd −1 (R 2 =0.71) and in late pregnancy was 12.8 gd −1 (R 2 =0.58), compared to the recommendation of 10 gd −1 . Nutritional regimes in pregnancy should account for changes in AA requirement during pregnancy to reduce the risk of overfeeding AA in early pregnancy and underfeeding AA in late pregnancy. Grant Funding Source : AB Pork, ON Pork, ALIDF, ACAAF, Ajinomoto

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.318
Teacher spread0.269 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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