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Record W4383736861 · doi:10.3390/dairy4030029

Relationship between Milk Yield and Udder Morphology Traits in White Fulani Cows

2023· article· en· W4383736861 on OpenAlexaff
Oladipupo Ridwan Bello, A. E. Salako, Adebayo Samson Akinade, Maaruf Yakub

Bibliographic record

VenueDairy · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsUdderMilkingAnimal scienceBiologyTraitLactationGenetic correlationVeterinary medicineMastitisMedicineGenetic variationGeneticsPregnancyGene

Abstract

fetched live from OpenAlex

The study examined the relationship between milk yield and udder morphology traits in White Fulani cows. Fifty-eight apparently healthy cows in early lactation at 2nd, 3rd, and 4th parity were used in the study. The data obtained from the cows were test day milk yield (TDMY) from single milking and udder morphology traits comprising udder length (UL), udder width (UW), udder depth (UD), fore teat length (FTL), rear teat length (RTL), fore teat diameter (FTD), and rear teat diameter (RTD). There was no significant effect of parity on TDMY or the udder morphology traits. Phenotypic correlations between TDMY, UL, UW, and UD were positive and significant. Notably, phenotypic correlations between UL and TDMY at different parities were the strongest. Teat measurements had no significant correlation with TDMY. Stepwise and principal component regressions were implemented to assess the relationship between milk yield and udder morphology traits. Interestingly, UL was the only trait that entered the reduced models. The results suggest a probable genetic correlation between milk yield and udder length. Therefore, since udder conformation traits are heritable, when selecting for udder length in White Fulani cows, a correlated response in milk yield is expected.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.420

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 head0.036
GPT teacher head0.267
Teacher spread0.231 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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