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Record W2597991937 · doi:10.2527/asasmw.2017.12.133

133 Young scholar presentation: Metabolic modifiers in beef cattle: Current application and future considerations

2017· article· en· W2597991937 on OpenAlexaff
B. M. Bohrer, A. C. Dilger, D. D. Boler

Bibliographic record

VenueJournal of Animal Science · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPresentation (obstetrics)Current (fluid)BiotechnologyBiologyMedicineEngineeringSurgeryElectrical engineering

Abstract

fetched live from OpenAlex

In the upcoming years, the expansion of global animal agriculture will be forced to keep pace with expected world population growth. Feeding the growing world will be accomplished by producing more food with fewer resources and improving efficiency in the methods in which food is currently being produced. This leaves great opportunity for the scientific community to improve efficiency in food and agricultural sciences. Metabolic modifiers can be used in livestock production to increase live efficiency and improve yields of animal-derived food products. Less is known about other benefits associated with the use of metabolic modifiers in beef cattle production. Multiple projects in our laboratory have focused on the use of metabolic modifier products in beef cattle, specifically ractopamine hydrochloride (RAC). Key outcomes of this research include production advantages in rate of gain, feed efficiency, and improvements in carcass yields in cattle fed RAC. The objectives of this research were to use commercially relevant approaches to answer applied research questions. Studies were conducted to analyze the effects of feeding cattle RAC with or without supplemental zinc and chromium and feeding cattle RAC with or without the ionophore monensin and the antibiotic tylosin phosphate. Rate of gain (9–16%), and feed efficiency (8–16%) during the finishing period were increased (P < 0.01) in cattle fed RAC compared with cattle not fed RAC. Carcass characteristics impacting quality and yield grade were minimally affected in cattle fed RAC compared with cattle not fed RAC. There were no differences (P > 0.05) in live animal performance or carcass traits with feeding supplemental zinc and chromium to cattle fed RAC. There were no differences (P > 0.05) in live animal performance or carcass traits when including or removing monensin and tylosin from the finishing diet of cattle fed RAC. In addition, research was dedicated to the effect of RAC on glucose and lipid metabolism parameters. Glucose and insulin were measured in non-fasted cattle. Glucose and insulin were measured after glucose-tolerance tests were conducted in cattle fed RAC (300 mg ractopamine·animal−1·d−1 for 21 d) in two studies. Only in one study were baseline and glucose-induced insulin levels reduced (P < 0.01) in cattle fed RAC compared with cattle not fed RAC. Overall, this research provided a brief sampling of strategies and possible considerations to be used with RAC in the beef cattle industry.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0460.008

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 head0.013
GPT teacher head0.296
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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