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Record W2904260579 · doi:10.1093/jas/sky404.879

79 Residual Feed Intake (RFI) Reranking in Beef Steers Fed Growing and Finishing Diets under Two Management Regimens.

2018· article· en· W2904260579 on OpenAlexaff
N.N. Ferriman, C. P. Campbell, Katharine M Wood, A. Michelle Edwards, I. B. Mandell

Bibliographic record

VenueJournal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsResidual feed intakeAnimal scienceBreedSilageCrossbreedBeef cattleBiologyPastureFeed conversion ratioBiotechnologyAgronomyBody weight

Abstract

fetched live from OpenAlex

A study (factorial design) was conducted to evaluate RFI reranking between growing and finishing phases of production using two nutritional management regimens (MR). One-hundred and eight non-implanted British and Continental crossbred steers (±353 kg) were fed an alfalfa/corn silage growing diet for ~90 days. RFI classification was calculated with steers classified as high, medium, and low (RFI) based on 0.5 SD from the mean. Cattle were allocated to two MR (MR1, MR2) based on RFI, breed, and weight. MR1 cattle were finished on an 84.7% concentrate diet while MR2 cattle were backgrounded on pasture for ~112 days and subsequently finished on the high concentrate diet. Steers were slaughtered at ~750 kg BW. Carcass and meat quality data were collected, and finishing phase RFI calculated to evaluate reranking. Average daily gains (ADG, kg/d) and gain:feed (G/F) respectively included: growing (±1.28; 0.178), MR1 finishing (±1.74; 0.152) and MR2 finishing (±1.92; 0.149). ADG differed between MR (P<0.001 but not amoungst RFI class (P > 0.05), while G/F differed between MR and RFI class (P<0.05). Approximately 74% of cattle changed RFI classification between growing and finishing phases; however, MR did not affect reranking (P=0.65). The odds ratio for reranking based on MR was 0.868, which suggests there is a lower chance of reranking amongst MR. MR2 cattle did however, have a higher proportion (16.7%) of steers rerank from high to low RFI. Spearman rank correlations for RFI reranking were not related to carcass weight (kg), yield (%), lean and fat (%)(P>0.05) while positively correlated with ribeye area (cm2) (P<0.001) and quality grade (P=0.02). Ribeye area was also smaller (P<0.05) for medium RFI steers when compared to either high or low RFI classification. Ultimately, management regimen did not affect reranking of RFI classification for cattle fed a common diet in the growing phase of production.

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)0.000
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.030
GPT teacher head0.273
Teacher spread0.244 · 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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Citations1
Published2018
Admission routes1
Has abstractyes

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