MétaCan
Menu
Back to cohort
Record W4317506837 · doi:10.3390/ani13030346

Impact of Dystocia on Milk Production, Somatic Cell Count, Reproduction and Culling in Holstein Dairy Cows

2023· article· en· W4317506837 on OpenAlexaffabout
S.M. Roche, Joseph A. Ross, Crystal Schatz, Kendall Beaugrand, Sjoert Zuidhof, Brenda Ralston, Nick Allan, Merle S. Olson

Bibliographic record

VenueAnimals · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsLakeland CollegeChinook Regional Hospital
Fundersnot available
KeywordsCullingReproductionSomatic cell countMilk productionDairy cattleAnimal scienceSomatic cellHolstein CattleBiologyLactationHerdPregnancyIce calvingEcologyGenetics

Abstract

fetched live from OpenAlex

This study investigated the effects of dystocia on milk production, somatic cell count, reproductivity, disease, and milk production. A total of 2159 cows across 21 dairy farms in Alberta, Canada were enrolled in this study. Multivariable models were created to explore associations between outcome variables and calving ease score. In total, 89.5% of calvings were unassisted, 6.1% were an easy pull, and 4.3% were a moderate-hard pull. Cows that had a moderate-hard pull produced 4.01 kg less milk, 0.12 kg less volume of milk fat, and 0.12 kg less milk protein per day than those that had an unassisted calving. No difference was found between calving ease groups with respect to SCC. Cows with a moderate or hard pull produced 510 kg less milk per lactation than unassisted cows. Cows with a moderate to high level of assistance at birth had a higher hazard of being culled over the duration of their lactation. Cows with an easy pull had increased odds of developing a retained placenta. It is evident that assistance at calving, particularly a moderate-hard pull, is associated with significant impacts on future milk production and risk of being culled; therefore, efforts should be made to minimize dystocia and prevent these impacts.

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)0.000
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 score0.902
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.279
Teacher spread0.244 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueAnimalsSame topicReproductive Physiology in LivestockFrench-language works237,207