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Record W2995049319 · doi:10.3168/jds.2018-16231

Identifying barriers to successful dairy cow transition management

2019· article· en· W2995049319 on OpenAlexaffabout
Katelyn E. Mills, Daniel M. Weary, M.A.G. von Keyserlingk

Bibliographic record

VenueJournal of Dairy Science · 2019
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStockingIce calvingPeriod (music)Work (physics)Dairy cattleTransition (genetics)Dairy industryTransition management (governance)BusinessAgricultural scienceAnimal scienceBiologyEcologySustainabilityFood scienceEngineeringLactation

Abstract

fetched live from OpenAlex

Many dairy cows become ill in the weeks after calving, a period when cows also experience numerous environmental and physiological changes. Most research on this transition period has focused on biological factors including nutrition, immunology, and physiology, but little work has examined sociological factors affecting how farmers care for their cows. The aim of the current study was to describe barriers preventing the adoption of more successful management practices. We used individual and group interviews, paired with photo elicitation, to understand the perspectives of farmers (n = 11) and veterinarians (n = 8) living and working in the lower Fraser Valley of British Columbia, Canada. Participants viewed transition period management as difficult. The lack of a single definition of the transition period emerged as one barrier to improvement; providing a clear and consistent definition for the transition period may be an important first step to improved practices on dairy farms. Participants also identified other barriers hindering improvement, including variation in both farmer attitude toward transition cow management and veterinarian involvement, stocking density of cows, and nutrition management. Barriers to improved practices varied by farm, suggesting that a tailored approach is required to make meaningful change.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.037
GPT teacher head0.336
Teacher spread0.299 · 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

Citations32
Published2019
Admission routes2
Has abstractyes

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