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Record W3005824862 · doi:10.1139/cjas2011-038

Removal of supplemental vitamin A from barley-based diets improves marbling in feedlot heifers

2011· article· en· W3005824862 on OpenAlexaff
D. J. Gibb, F. H. Van Herk, P. S. Mir, S. C. Loerch, Tim A. McAllister

Bibliographic record

VenueBioOne Complete (BioOne) · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMarbled meatFeedlotAnimal scienceDry matterVitaminMedicineBiologyEndocrinology

Abstract

fetched live from OpenAlex

Gibb, D. J., Van Herk, F. H., Mir, P. S., Loerch, S. and McAllister, T. A. 2011. Removal of supplemental vitamin A from barley-based diets improves marbling in feedlot heifers. Can. J. Anim. Sci. 91: 669-674. The objective of this research was to determine if removing supplemental vitamin A from barley-based feedlot diets affects animal performance, health, or carcass quality. Six pens per treatment (10 heifers per pen) were randomly assigned to receive zero (-VA) or 3640 (+VA) IU kg-1 dry matter of supplemental vitamin A in barley-based feedlot diets. Initial serum retinol was similar between treatments (28 µg dL-1; P=0.34), but -VA reduced levels by 40% (30 vs. 50 µg dL-1; P<0.001) by day 217. Removal of supplemental vitamin A reduced dry matter intake during the 58 d backgrounding period (6.93 vs. 7.07 kg d-1; P=0.007) and over the 218-d trial (9.18 vs. 9.35 kg d-1; P<0.001), but had no effect on average daily gain during backgrounding (1.22 kg d-1; P=0.46) or over all (1.46 kg d-1; P=0.15). Based on camera grading, -VA increased degree of marbling (480.6 vs. 439.3; P=0.02) without affecting backfat thickness (0.74 cm; P=0.62). Ultrasound measurements were highly correlated with camera grading, but did not detect treatment difference in marbling score (P=0.99). Results from this study show that the removal of supplemental vitamin A increased marbling without affecting backfat, gains, or animal health.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.999

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)0.0020.000

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 head0.330
GPT teacher head0.232
Teacher spread0.098 · 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.

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
Published2011
Admission routes1
Has abstractyes

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