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Feasibility of a Linear Scoring Method of Udder Morphology for the Selection Scheme of Sardinian Sheep

2006· article· en· W2080178581 on OpenAlexfundno aff
Sara Casu, I. Pernazza, A. Carta

Bibliographic record

VenueJournal of Dairy Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsnot available
FundersInstitute of Infection and ImmunityInterreg
KeywordsUdderSireSelection (genetic algorithm)BiologyAnimal scienceTraitLactationHeritabilityGenetic correlationBest linear unbiased predictionRestricted maximum likelihoodVeterinary medicineFlockRepeatabilityStatisticsMathematicsGenetic variationMastitisGeneticsMedicineMaximum likelihoodGene

Abstract

fetched live from OpenAlex

Breeders are increasingly interested in improving the machine milkability of Sardinian dairy sheep by selection for udder morphology. Nine-point linear scales were developed to appraise teat placement, degree of suspension of the udder, udder depth, and degree of separation of the 2 halves. Repeatabilities within and across lactation were estimated on an experimental flock. Ewes were scored at least 3 times a year from 1999 to 2003. Within-lactation repeatabilities were greater than 0.70 for all linear traits, whereas across-lactation repeatabilities ranged from 0.59 to 0.66. The first linear combination of basic traits, obtained by principal component analysis, was highly correlated with teat placement, degree of suspension of the udder, and udder depth and showed an across-lactation repeatability of 0.76. These results indicate that an accurate evaluation of animals is possible by a single, early lifetime score. Genetic parameters of linear udder traits were estimated using a REML method applied to a sire model. Data were selected from 76,984 scores of Sardinian yearling ewes collected by 31 classifiers from 1999 to 2004. Two models were compared, one fitting the contemporary group effect as fixed and the other as random. Heritabilities of udder traits ranged from 0.19 to 0.31. The model with the random contemporary group effect produced slightly higher heritabilities and higher correlations between the sires' estimated breeding values and the daughters' average scores. As a whole, the genetic correlations between udder traits were favorable, indicating that selection for one trait will produce a positive evolution of the overall udder conformation. In particular, the degree of suspension of the udder was highly correlated with udder depth (0.82). Genetic correlations with milk yield were unfavorable but generally low, with the exception of udder depth (-0.48). Genetic trends were estimated using an animal model. Only udder depth showed a negative constant genetic trend. Overall results indicated that genetic improvement of the udder morphology of Sardinian ewes is feasible, with major emphasis on teat placement and degree of udder suspension, traits showing the highest heritabilities and low unfavorable genetic correlations with milk yield.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.066
GPT teacher head0.334
Teacher spread0.268 · 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 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

Citations63
Published2006
Admission routes1
Has abstractyes

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