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Record W2982127552 · doi:10.1139/cjas-2019-0053

Generation intervals in Canadian dairy cattle herds

2019· article· en· W2982127552 on OpenAlexaffvenueabout
Bernard Ato Hagan, R.I. Cue

Bibliographic record

VenueCanadian Journal of Animal Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsMcGill University
FundersGhana Education Trust Fund
KeywordsHerdBreedArtificial inseminationAnimal scienceBrown SwissDairy cattleBiologySelection (genetic algorithm)GeographyPregnancy

Abstract

fetched live from OpenAlex

Genetic evaluation records for the Canadian Holstein, Ayrshire, Jersey, and Brown Swiss bulls and cows born from 1950 and 1960, respectively, were used to study the generation intervals (L) along the four-path selection model. The objectives of the study were to determine the L in the four dairy cattle breeds and the effects of some environmental factors and variations among herds or artificial insemination (AI) studs on the L achieved. Total L of the four selection paths was reduced by 55% from 29.2 yr in 1980 to 13.2 yr in 2016 in the Holstein breed. Substantial reductions in total L were also observed in the Ayrshire, Jersey, and Brown Swiss populations between 1980 and 2016. Unlike progeny year of birth, housing system, agricultural region in Québec (region) — as well as their interaction — were not important on realised L of sires and dams used on Canadian dairy farms. There were significant variations among herds and AI studs on the age of sires and dams used for breeding. The considerable variations in realised L among herds and AI studs offer opportunities to increase the annual rate of genetic progress in the four Canadian dairy cattle populations.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.277
Threshold uncertainty score0.556

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.254
Teacher spread0.235 · 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 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

Citations8
Published2019
Admission routes3
Has abstractyes

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