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Record W2885039808 · doi:10.3168/jds.2017-14115

Exposure to an unpredictable and competitive social environment affects behavior and health of transition dairy cows

2018· article· en· W2885039808 on OpenAlexafffund
Kathryn L. Proudfoot, Daniel M. Weary, S.J. LeBlanc, L.K. Mamedova, 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
FundersNatural Sciences and Engineering Research Council of CanadaNovus InternationalZoetisDairy Farmers of Canada
KeywordsIce calvingAnimal scienceMorningDairy cattleBiologyLactationPregnancy

Abstract

fetched live from OpenAlex

Social factors are important determinants of disease in humans and and laboratory animals, but less research has been done using farm animals. The objective of this study was to determine if an unpredictable and competitive social environment affects behavior and health during the transition period when dairy cows are at high risk of disease. Five weeks before calving, 64 cows were assigned to a predictable and noncompetitive social environment (predictable) or an unpredictable and competitive social environment (unpredictable) using 8 groups of 4 animals per treatment. Each group consisted of 3 multiparous and 1 primiparous cow. At first enrollment (baseline; 5 wk before calving), all groups had access to 4 electronic feed bins. At 4 wk before calving, cows in the predictable groups were given access to 6 feed bins, and cows in the unpredictable groups were moved into a new pen with 4 resident cows each trained to consume feed from one bin. Each cow in the unpredictable group was then provided access to only 1 of the 4 feed bins which they shared with 1 resident cow (resulting in 2 cows/bin), creating a competitive feeding environment. To create an unpredictable environment, access to morning feed was delayed 0, 1, 2, or 3 h every other day. On alternate days, the cows in unpredictable groups were assigned to feed from a new feed bin (and thus had to compete with a new resident partner). Feeding and social behavior were collected electronically from the feed bins. Blood was sampled at baseline (wk -5), wk -2, wk -1, and wk +1 relative to calving to measure inflammatory (haptoglobin and tumor necrosis factor-α) and metabolic (nonesterified fatty acids, β-hydroxybutyrate, calcium, and glucose) biomarkers. Uterine cytology was performed 3 to 5 wk after calving to diagnose cytological endometritis. Data were analyzed using mixed models including baseline data as a covariate, week as a repeated measure, treatment as a main effect, and a treatment by week interaction. The probability of cytological endometritis at the group level was analyzed using Mann-Whitney U tests. Parity was included in separate models to determine any parity × treatment interactions. Cows from both treatments consumed the same amount of feed, but cows in the unpredictable group spent less time feeding and had a higher rate of feed intake. Cows in the unpredictable groups also visited the feed bins less often, consumed more feed during each visit, and were involved in more social replacements at the feed bin compared with predictable groups. Cows in the unpredictable groups had higher serum concentrations of nonesterified fatty acids and tumor necrosis factor-α, but lower β-hydroxybutyrate compared with predictable groups. Multiparous cows in unpredictable groups were more likely to be diagnosed with cytological endometritis after calving compared with cows in the predictable groups, but primiparous cows in unpredictable groups showed a tendency for the opposite response. These results suggest that an unpredictable and competitive social environment before calving causes changes in feeding and social behavior, some physiological indicators of metabolism and inflammation, and increases the risk of uterine disease in multiparous cows after calving.

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 score0.973
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

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