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Record W4402533644 · doi:10.1093/jas/skae234.656

PSVIII-11 Cold stress responses in beef heifers with divergent residual feed intake

2024· article· en· W4402533644 on OpenAlexaffabout
María Camila Londoño-Méndez, Sergio David Lasso-Ramirez, Carolyn Fitzsimmons, Graham Plastow, Edward W. Bork, J. A. Basarab, Gleise Medeiros da Silva

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsResidual feed intakeAnimal scienceBeef cattleCold stressBiologyBody weightFeed conversion ratioEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Beef cattle have been selected for feed efficiency to reduce feeding costs and environmental impact. Still, there is a paucity of knowledge on how feed-efficient beef females maintained outdoors respond to extreme cold weather. Therefore, this research assessed blood parameters and rumen temperature (RT) of beef heifers with divergent residual feed intake (RFI) during winter in Alberta, Canada. Ccrossbred beef heifers [n = 41; body weight (BW) = 474 ± 38; approximately 21 mo of age) previously tested for RFI in drylot and classified as either more (n = 21; LOW-RFI = -1.0 ± 0.70) or less feed-efficient (n = 20; HIGH-RFI = 1.4 ± 1.00) were used in a completely randomized design for 55 d (January to March). Heifers were maintained in a single dormant pasture and received free-choice hay. A Smart Rumen Bolus (Moonsyst) was used to automatically record RT. Blood samples were collected every 18 ± 8 d based on weather conditions to determine concentrations of blood urea nitrogen (BUN), non-esterified fatty acids (NEFA), insulin-like growth factor 1 (IGF-1), β-Hydroxybutyric acid (BHBA), leptin (LEP), free triiodothyronine (fT3), haptoglobin (HP), heat shock protein 70 (HSP70), gamma-aminobutyric acid (GABA), and serotonin (5-HT). Environmental conditions were assessed by calculating the Climate Comprehensive Index (CCI) using temperature, wind speed, solar radiation, and humidity from a weather station within 1 km of the pasture. Based on CCI, daily weather conditions were considered to impose mild, moderate, severe, extreme, and extremely dangerous stress risk for 3, 15, 19, 13, and 5 d of the study, respectively. Leptin was greater in HIGH vs. LOW-RFI heifers (P = 0.05; 5.21 vs. 4.56 ng/ml). A tendency for an RFI × day interaction was detected for GABA (P = 0.09) and HP (P = 0.06), with greater concentrations in the LOW-RFI heifers on extreme and extremely dangerous cold days, respectively. However, HP concentrations were below the threshold for inflammation throughout the study. An effect of day was detected for IGF-1, LEP, BUN, NEFA, BHB, and 5-HT (P ≤ 0.001). The least concentrations of BHBA and BUN (128 nmol/L and 4.8 mg/dL, respectively) were recorded, along with the greatest NEFA concentration (0.619 mEq/L), during an extremely dangerous cold day. On extreme cold days, LEP (3.7 ug/L) was the least and 5-HT was the greatest (56.7 ng/mL). Rumen temperature was greater in LOW-RFI vs. HIGH-RFI when day imposed greater risks to cause cold stress (P = 0.01). However, no differences were detected in final BW or average daily gain (P ≥ 0.24). In summary, results indicate differential blood parameter dynamics and rumen temperature fluctuations between high and low RFI heifers under varying cold stress conditions. Greater plasma leptin concentrations and decreased rumen temperature are likely associated with heightened thermogenic activity and cold stress in less-feed-efficient heifers.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.242
Teacher spread0.225 · 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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