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Record W4313328948 · doi:10.3168/jds.2022-22534

Invited review: Social dominance in dairy cattle: A critical review with guidelines for future research

2022· review· en· W4313328948 on OpenAlexafffund
Joseph Krahn, Borbala Foris, Daniel M. Weary, M.A.G. von Keyserlingk

Bibliographic record

VenueJournal of Dairy Science · 2022
Typereview
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaDairy Farmers of ManitobaDairy Farmers of Canada
KeywordsDominance (genetics)Agonistic behaviourDominance hierarchySocial psychologyPsychologyBiologyAggression

Abstract

fetched live from OpenAlex

Cattle are gregarious animals able to form social relationships. Dominance is one of the most widely studied social behaviors of dairy cattle, especially cows confined indoors. However, much of the past dairy cattle research has used an unstandardized approach, differing in definitions and conceptual understanding of dominance, as well as their methods of data collection and dominance calculation. The first of the 3 aims of this review is to evaluate how dominance relates to the social behavior of housed dairy cows. Cows engage in agonistic interactions to establish and reinforce dominance relationships. An individual's dominance is influenced by intrinsic characteristics, such as personality, and extrinsic factors, including group composition. When competing for resources, agonistic interactions can also be influenced by individual motivational differences, such as hunger, which may diminish the role of dominance in regulating competition. Our second aim is to critically review methods used to assess dominance in cows. This includes discussions on the effect of time and location of data collection on measured values as well as the viability and limitations of some dominance calculation methods. We propose that different methodologies lend themselves to different types of research questions. For example, the use of data stream-based methods that consider the sequence of interactions are useful for estimating how dominance fluctuates with changing conditions and can be used in a dynamically changing group. In contrast, matrix-based methods that aggregate social interactions may be best for identifying the social position of individuals and understanding how social characteristics influence the attributes of a stable hierarchy. Our third aim is to discuss the future of dominance research. We use a flowchart to illustrate guidelines for a more standardized approach to measuring dominance in cattle. We also identify areas in need of further conceptual clarification, suggest practical applications of dominance when managing dairy cattle, and discuss some limitations of dominance 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 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)0.013
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.002
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 head0.463
GPT teacher head0.562
Teacher spread0.100 · 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.

Study designNot applicable
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

Citations26
Published2022
Admission routes2
Has abstractyes

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