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Record W3094409508 · doi:10.3168/jds.2020-19043

Invited review: Academic and applied approach to evaluating longevity in dairy cows

2020· review· en· W3094409508 on OpenAlexaff
Jesse C. Schuster, Herman W. Barkema, Albert De Vries, D.F. Kelton, Karin Orsel

Bibliographic record

VenueJournal of Dairy Science · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of GuelphUniversity of Calgary
Fundersnot available
KeywordsLongevityAnimal scienceDairy cattleBiologyMedicineGerontology

Abstract

fetched live from OpenAlex

In its simplest form, longevity is defined as the ability to live a long life. Within the dairy industry, longevity has been defined and measured in many different ways, and the aim of this review is to disentangle the definitions and provide some clarity. Using a more standardized approach for defining and measuring longevity, both in academic discussions and on-farm application, we suggest using herd life (days) for time from birth until culling, and length of productive life (days) for time from first calving until culling. Despite identified benefits of extending the length of productive life, global trends in the time spent by dairy cattle in the herd have mostly been negative. Factors influencing herd life, such as health, rearing, environmental conditions, and management, are often ignored when longevity goals are evaluated, thereby underestimating the effect these factors have on defining overall longevity. Also, production efficiency, herd profitability, and welfare are not necessarily served by the longest life but rather by the optimized length of herd life instead. The majority of research has focused on the role of genetics on longevity. In this review, we provide insight into influences affecting dairy cow herd life as well as farm- and cow-level factors associated herewith. Finally, we suggest using herd life, including reproduction, production, health, and youngstock performance, for farm-level evaluation and length of productive life for time spent in the lactating herd.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.360
Teacher spread0.294 · 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 designOther design
Domainnot available
GenreReview

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

Citations112
Published2020
Admission routes1
Has abstractyes

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