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Record W4389502426 · doi:10.3168/jdsc.2023-0474

Methods used for estimating sleep in dairy cattle

2023· article· en· W4389502426 on OpenAlexafffund
Kathryn L. Proudfoot, Emma Ternman

Bibliographic record

VenueJDS Communications · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of CanadaHelsingin Yliopisto
KeywordsDairy cattleSleep (system call)PolysomnographyDairy industryNarrative reviewAnimal sciencePsychologyMedicineBiologyComputer scienceFood scienceApneaIntensive care medicinePsychiatry

Abstract

fetched live from OpenAlex

Sleep serves several essential functions in all mammals including dairy cattle. Researchers are beginning to estimate sleep in dairy cattle using a combination of physiological measurements (e.g., polysomnography) as well as changes in behavior (e.g., different resting postures). Sleep may provide unique insight into how cows and calves respond to, and cope with, their environments, as a complement to other common measurements such as lying time. Although each of the methods to assess sleep in cattle has its advantages, there remain several challenges with each approach. The objective of this narrative mini-review is to describe current methods for estimating sleep in dairy cattle, including some of the advantages and limitations with each method. We will start with describing the research to date on adult cows, followed by preweaning dairy calves. We end the review with recommendations for researchers interested in assessing sleep in dairy cattle and ideas for future areas of research.

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)0.012
metaresearch head score (Gemma)0.025
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.296
GPT teacher head0.516
Teacher spread0.219 · 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
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

Citations3
Published2023
Admission routes2
Has abstractyes

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