MétaCan
Menu
Back to cohort
Record W1992148742 · doi:10.2527/jas.2005-715

Test duration for growth, feed intake, and feed efficiency in beef cattle using the GrowSafe System1

2006· article· en· W1992148742 on OpenAlexaffabout
Z. Wang, J. D. Nkrumah, Chengdao Li, J. A. Basarab, L. A. Goonewardene, E. K. Okine, D. H. Crews, S. S. Moore

Bibliographic record

VenueJournal of Animal Science · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsAgriculture Food and Rural DevelopmentAgriculture and Agri-Food CanadaUniversity of Alberta
Fundersnot available
KeywordsResidual feed intakeAnimal scienceBeef cattleFeed conversion ratioResidualMathematicsStatisticsBiologyBody weight

Abstract

fetched live from OpenAlex

This study was conducted to determine the optimum test duration and the effect of missing data on accuracy of measuring feed efficiency and its 4 related traits ADG, DMI, feed conversion ratio, and residual feed intake in beef cattle using data from 456 steers with 5,397 weekly averaged feed intakes and BW repeated measurements taken over 91 d. Data were collected using the GrowSafe System at the University of Alberta Kinsella Research Station. The changes and relative changes in phenotypic residual variances and correlations (Pearson and Spearman) among data from shortened test durations (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, or 84 d) and a 91-d test were used to determine the optimum test duration for the 4 traits. The traits were fitted to a mixed model with repeated measures using SAS. Test durations for ADG, DMI, feed conversion ratio, and residual feed intake could be shortened to 63, 35, 42, and 63 d, respectively, without significantly reducing the accuracy of the tests when BW was measured weekly. The accuracy of the test was not compromised when up to 30% of the records were randomly removed after the first 35 d on test. These results have valuable and practical implications for performance and feed efficiency testing in beef cattle.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.827
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations144
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Animal ScienceSame topicGenetic and phenotypic traits in livestockFrench-language works237,207