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Record W2328565435 · doi:10.4141/cjas-2014-060

Colostrum production in sows fed different sources of fiber and fat during late gestation

2015· article· en· W2328565435 on OpenAlexvenueno aff
Uffe Krogh, Thomas Sønderby Bruun, C. Amdi, Christine Flummer, J. S. D. Poulsen, Peter Kappel Theil

Bibliographic record

VenueCanadian Journal of Animal Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsColostrumGestationAnimal scienceFiberBiologyFood sciencePregnancyChemistryImmunology

Abstract

fetched live from OpenAlex

Krogh, U., Bruun, T. S., Amdi, C., Flummer, C., Poulsen, J. and Theil, P. K. 2015. Colostrum production in sows fed different sources of fiber and fat during late gestation. Can. J. Anim. Sci. 95: 211–223. The objective was to study yield and composition of colostrum and transient milk from 36 second-parity sows fed a standard lactation diet (CON) low in fiber or one of two high-fiber diets based on sugar beet pulp (SBP) or alfalfa meal (ALF), combined with one of three fat sources, palm fatty acid distillate (PFAD), soybean oil (SOYO) or trioctanoate (C8TG) from day 105 of gestation onward. Sows were milked at 0, 12, 24 and 36 h relative to onset of parturition. Jugular vein blood was collected on day 112 of gestation. Plasma acetate content was affected by dietary fiber and fat treatment (P<0.05), indicating altered intermediary metabolism. Colostrum yield, predicted from piglet birth weight, suckling duration and weight gain, was unaffected by dietary treatments (P>0.10). Colostral (24 h) and transient milk dry matter contents were greater in SOYO compared with PFAD and C8TG sows (P<0.05). Colostrum (12 and 24 h) and transient milk lactose contents were greatest in CON-fed sows compared with sows fed ALF or SBP diets (P<0.05). In conclusion, nutrition in late gestation affected the intermediary metabolism and colostrum composition, but did not affect colostrum yield of sows.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.029
GPT teacher head0.227
Teacher spread0.198 · 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 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

Citations52
Published2015
Admission routes1
Has abstractyes

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