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Record W3132285094 · doi:10.1111/jbg.12540

Estimation of economic value for efficiency and animal health and welfare traits, teat and udder structure, in Canadian Angus cattle

2021· article· en· W3132285094 on OpenAlexafffundabout
Kajal Devani, Cheryl Quinton, J. A. Archer, Bruno Santos, Daniel Collado, P.R. Amer, Ed Pajor, Karin Orsel, John J. Crowley

Bibliographic record

VenueJournal of Animal Breeding and Genetics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsAlberta InnovatesUniversity of AlbertaUniversity of Calgary
FundersAlberta Innovates Bio Solutions
KeywordsUdderSireHeritabilitySelection (genetic algorithm)Best linear unbiased predictionEstimationAnimal welfareWelfareBiologyGenetic correlationGenetic gainStatisticsAnimal scienceGenetic variationEconomicsMathematicsMastitisGeneticsComputer science

Abstract

fetched live from OpenAlex

The Canadian Angus Association recently developed genetic evaluations for teat and udder structure, which impact efficiencies, and animal health and welfare. Genetic selection tools are most effective incorporated into economic selection indexes. An important factor in the development of economic indexes is the estimation of the economic value and discounted gene expression coefficients, and thereby the economic weight, of each trait. Traditional estimation methods, interrogation of previous studies quantifying the impact of the traits and bioeconomic modelling, were reinforced using producer surveys that employed pairwise ranking methods. Estimates of discounted genetic expression coefficients, economic value and economic weight for teat and udder score in Canadian Angus cattle were 0.31 per sire, $52.47, and $16.91 per score change on a per calf born basis, respectively, indicating that functional traits such as teat and udder structure have a significant impact on profitability and should be included in genetic selection programmes. Limitations in previous studies illustrate the need for longitudinal studies on traits that impact efficiencies and animal health and welfare.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.382

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)0.0000.000

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 head0.011
GPT teacher head0.260
Teacher spread0.250 · 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 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".

Quick stats

Citations4
Published2021
Admission routes3
Has abstractyes

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