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Record W2790667207

Prediction of scrotal circumference expected progeny differences in limousin cattle

2015· dissertation· en· W2790667207 on OpenAlexaboutno aff
L.L. Keeton

Bibliographic record

VenueThinkTech (Texas Tech University) · 2015
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsCircumferenceNull (SQL)BiologyAnimal scienceVeterinary medicineStatisticsGeographyMathematicsMedicineComputer scienceData mining
DOInot available

Abstract

fetched live from OpenAlex

In the first phase, 557 Limousin bulls were placed on a Canadian
\nLimousin Association postweaning gain test over 4 yr (1987 to 1991).
\nBulls varied in age by 120 d. Weights (WT) and scrotal circumferences
\n(SC) were collected at 28 d intervals from shortly after weaning to the end
\nof the test on each bull (6 or 7 measurements per year). Hip heights (HT)
\nwere collected on the second and again on the final measurement dates
\neach year. Age in days (AGE) was calculated for each measurement date.
\nAge of dam (AOD) was also recorded for each bull on test. Correlations of
\nSC with AGE, WT and HT were high and positive (P < .0 1). Year and AOD
\neffects were present (P < .001). Quadratic regression equations were
\nestimated for AGE, WT and HT on SC to develop adjustment equations.
\nThe resulting regression equation for the estimation of SC from AGE was:
\nY = -3.02 + .131(AGE)- .000114(AGE)2 (R2 = 74.8°/o, P < .001). Age-ofdam
\nadjustment factors were determined by use of indicator variables.
\nAnalyses showed that age adjusted SC should be adjusted to a 4- to 10-
\nyear AOD equivalent by adding .51, .31 or .36 em for dams 2, 3 or> 11
\nyears of age (P < .05). Adjustment of scrotal circumference in Limousin
\nbulls to a constant, for subsequent use in national cattle evaluation, can
\nbest be achieved by removing some of the variation in scrotal
\ncircumference caused by environmental effects of age of dam and age,
\nweight or height.
\nNext, variance and covariance components were estimated for
\nscrotal circumference and weaning weight from Limousin field data.
\nRecords of 8,226 bulls were used to evaluate 590 sires and 661 maternal grand sires. Data included all herd book records of bulls with a recorded
\nscrotal circumference and their weaning contemporaries. Analyses were
\ncarried out by REML techniques employing the EM algorithm and fitting
\nboth single- and two-trait models. Scrotal circumference was first fitted
\nin a single-trait, sire model to obtain starting values for variances for a
\nlater analysis. Likewise, weaning weight was fitted in a single-trait, sirematernal
\ngrandsire model to obtain starting values for (co)variances for a
\nlater analysis. Scrotal circumference and weaning weight were then
\nfitted together in a two-trait model to estimate variance components.
\nEstimates of variance components were calculated by equating
\n(co)variances obtained from the models to their expectations. Estimates
\nof heritability of scrotal circumference, direct weaning weight and
\nmaternal weaning weight were .46, .25 and .19, respectively. Estimates
\nof genetic correlations between scrotal circumference and direct weaning
\nweight, scrotal circumference and maternal weaning weight, and direct
\nweaning weight and maternal weaning weight were .14, -.22 and -.44,
\nrespectively. The estimate of the environmental correlation between
\nscrotal circumference and weaning weight was .59.
\nFinally, the genetic parameters obtained were utilized to predict
\nbreeding values. Records on 9,618 bulls were available for the prediction
\nof breeding values. Two-trait, reduced animal mixed model equations for
\na maternally influenced trait were utilized and solved by BLUP
\nprocedures to predict the breeding values. A genetic trend in scrotal
\ncircumference for animals born from 1971 to 1989 was not discernible in
\nthese data.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.221
Teacher spread0.198 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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