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Record W2788515020 · doi:10.3168/jds.2017-13078

Behavioral changes before metritis diagnosis in dairy cows

2018· article· en· W2788515020 on OpenAlexaff
Heather W. Neave, J. Lomb, Daniel M. Weary, S.J. LeBlanc, J.M. Huzzey, M.A.G. von Keyserlingk

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of GuelphUniversity of British Columbia
FundersElanco Animal Health
KeywordsMetritisIce calvingLamenessAnimal scienceKetosisMedicineVaginal dischargeMastitisDairy cattleVeterinary medicineLactationBiologyPregnancySurgeryEndocrinology

Abstract

fetched live from OpenAlex

Metritis is common in the days after calving and can reduce milk production and reproductive performance. The aim of this study was to identify changes in feeding and social behavior at the feed bunk, as well as changes in lying behavior before metritis diagnosis. Initially healthy Holstein cows were followed from 3 wk before to 3 wk after calving. Behaviors at the feed bunk were recorded using an electronic feeding system. Lying behavior was recorded using data loggers. Metritis, based upon the characteristics of vaginal discharge at d 3, 6, 9, 12 and 15 after calving, was diagnosed in 74 otherwise healthy cows. Behavior of these cows, beginning 2 wk before calving until the day of diagnosis, was compared with 98 healthy cows (never diagnosed with any health disorder, including ketosis, mastitis, and lameness) during the transition period. During the 2 wk before calving, cows later diagnosed with metritis had reduced lying time and fewer lying bouts compared with healthy cows. In the 3 d before clinical diagnosis, cows that developed metritis ate less, consumed fewer meals, were replaced more often at the feed bunk, and had fewer lying bouts of longer duration compared with healthy cows. We concluded that changes in feeding as well as social and lying behavior could contribute to identification of cows at risk of metritis.

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 score0.720
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.047
GPT teacher head0.300
Teacher spread0.253 · 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

Citations65
Published2018
Admission routes1
Has abstractyes

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