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Record W2944882762 · doi:10.1093/jas/skz171

Level and source of fat in the diet of gestating beef cows: I. Effects on the prepartum performance of the dam and birth weight of the progeny1

2019· article· en· W2944882762 on OpenAlexaff
F. Añez-Osuna, G.B. Penner, John Campbell, M.E.R. Dugan, Carolyn Fitzsimmons, P. G. Jefferson, H.A. Lardner, J. J. McKinnon

Bibliographic record

VenueJournal of Animal Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsAnimal scienceIce calvingBeef cattleStrawHayRandomized block designConceptusCanolaBiologyGestationPregnancyLactationFood scienceAgronomy

Abstract

fetched live from OpenAlex

A 2-yr study was conducted to evaluate the effects of level and source of fat in the diet of gestating beef cows on their prepartum performance and birth weight of progeny. Each year, 75 multiparous (≥3 calving) pregnant Angus cows were stratified by BW (663 ± 21.5 kg) and BCS (2.6 ± 0.12; 1 to 5 scale) and randomly assigned to 1 of 15 outdoor pens. Subsequently, each pen was randomly assigned to 1 of 3 (n = 5) treatments: a low-fat diet (LF; 1.4 ± 0.12% EE) consisting of grass-legume hay, barley straw, and barley grain, or 1 of 2 high-fat diets (HF; 3.3 ± 0.20% EE) that included either a canola seed (CAN) or a flaxseed (FLX) based pelleted feed. Diets were formulated to meet the requirements of pregnant beef cows during the last 2 trimesters of gestation (0.183 ± 4.8 d), adjusted for changes in environmental conditions, and offered such that each pen on average received similar daily amounts of DE (31.2 ± 2.8 Mcal/cow), CP (1.36 ± 0.13 kg/cow), and DM (12.9 ± 1.0 kg/cow). Data were analyzed as a randomized complete block design with contrasts to separate the effects of level (LF vs. HF) and source (CAN vs. FLX) of fat. After 160 d on trial, conceptus corrected-BW (CC-BW) of LF cows (708 kg) and the proportion of overconditioned cows (13.2%) were greater (P ≤ 0.04) than those of HF, with no difference (P ≥ 0.84) between CAN and FLX for CC-BW (697 kg) and proportion of overconditioned cows (3.6% vs. 2.9%). Feeding FLX diet during gestation resulted in cows with a greater (P ≤ 0.01) concentration of conjugated linolenic acid (0.12% vs. 0.05%) and n-3 (0.58% vs. 0.37%) fatty acids, and a tendency (P = 0.09) for conjugated linoleic acid concentration (1.05% vs. 0.88%) to be greater in subcutaneous adipose tissue (SCAT) when compared with cows fed the CAN diet. By the end of gestation, serum NEFA concentration of LF cows (592 µEq/L) was lower (P < 0.01) than that of HF cows, and FLX cows had greater (P < 0.01) serum NEFA concentration than CAN cows (636 vs. 961 µEq/L). Cows receiving the LF diet during gestation gave birth to lighter (P < 0.01) calves compared with those receiving the HF diets (40.2 vs. 42.9 kg), with no difference (P = 0.24) between calves born to CAN (42.4 kg) and FLX (43.3 kg) cows. In conclusion, these results suggest a partitioning of the ME in pregnant beef cows that is dependent on the type of dietary energy, resulting in heavier calves at birth for cows fed high-fat diets. Also, the type of fatty acid in the diet of gestating beef cows affected the fatty acid profile in SCAT and serum NEFA concentration.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

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)0.0010.000

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 head0.020
GPT teacher head0.230
Teacher spread0.210 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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