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Record W2150270381 · doi:10.4141/cjas-2014-184

Effect of post-weaning residual feed intake classification on grazed grass intake and performance in pregnant beef heifers

2015· article· en· W2150270381 on OpenAlexafffundvenue
G. Manafiazar, J. A. Basarab, V. S. Baron, Lisa McKeown, Raquel R. Doce, M. L. Swift, M. Undi, K. M. Wittenberg, Kim Ominski

Bibliographic record

VenueCanadian Journal of Animal Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of ManitobaAgriculture and Agri-Food CanadaAgriculture Food and Rural DevelopmentUniversity of Alberta
FundersAgriculture and Agri-Food CanadaAlberta Livestock and Meat AgencyUniversity of AlbertaAlberta Beef ProducersUniversity of ManitobaManitoba Rural Adaptation Council
KeywordsResidual feed intakePastureAnimal scienceBeef cattleForageWeaningCrossbreedBiologyGrazingFeed conversion ratioAgronomyBody weight

Abstract

fetched live from OpenAlex

Manafiazar, G., Basarab, J. A., Baron, V. S., McKeown, L., Doce, R. R., Swift, M., Undi, M., Wittenberg, K. and Ominski, K. 2015. Effect of post-weaning residual feed intake classification on grazed grass intake and performance in pregnant beef heifers. Can. J. Anim. Sci. 95: 369–381. There is limited knowledge of how cattle tested for feed efficiency under drylot conditions perform when they graze on summer pasture. Residual feed intake adjusted for end of test backfat thickness (RFIfat) was determined on 171 beef crossbred heifers under drylot conditions over 2 yr using an automated system. Upon completion of the test, the 10 lowest and 10 highest RFIfat (–0.54±0.17 vs. 0.58±0.15 kg DM d−1) heifers in 2012, and the 14 lowest and 14 highest RFIfat (−0.47±0.16 vs. 0.53±0.19 kg DM d−1) heifers in 2013 were selected and placed on meadow bromegrass pasture to investigate the effect of RFIfat ranking on their grass intake and performance on the pasture. The pasture adaptation period (8 d in 2012 and 19 d in 2013) was followed by a pasture feed intake experiment during which heifers were dosed twice daily (0815 and 1415) with 500 g of C32-labeled feed pellet for 13 d (day 0 to 12) and fecal sampled twice daily (0815 and 1415) from day 8 to 12. Forage DM intake on pasture for each heifer was determined using the double alkane (C31/C32) methodology. High and low RFIfat heifers were similar in body weight (BW), backfat and rump fat thickness, and average daily gain (ADG) during the grazing trial period, except backfat thickness at the end of test period. However, low RFIfat heifers consumed 5.3% less forage when expressed as kg DM d−1 (8.20±0.08 vs. 8.66±0.09, P<0.001) and 5.1% less when expressed as a percentage of body weight (1.86±0.02 vs. 1.96±0.02% of BW, P<0.001) compared with high RFIfat heifers. RFIfat measured under drylot conditions in growing heifers was positively correlated to grazed RFIfat determined in pregnant heifers (rp=0.30, P=0.04). These results suggest that beef heifers classified as low RFIfat during the post-weaning drylot period had lower dry matter intake as heifers in their first pregnancy grazing tame pasture, with no negative impact on their body weight, back-fat thickness, and ADG compared with their high RFIfat herdmates.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.034
GPT teacher head0.249
Teacher spread0.215 · 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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Citations42
Published2015
Admission routes3
Has abstractyes

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