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Record W1967425601 · doi:10.1155/2014/938327

Understanding the Behavior of Domestic Emus: A Means to Improve Their Management and Welfare—Major Behaviors and Activity Time Budgets of Adult Emus

2014· article· en· W1967425601 on OpenAlexafffund
Deepa G. Menon, Darin C. Bennett, Kimberly M. Cheng

Bibliographic record

VenueJournal of Animals · 2014
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaBritish Columbia Ministry of Agriculture and Lands
KeywordsPsychology

Abstract

fetched live from OpenAlex

Information on domestic emu behavior is sparse and hence a study was undertaken to identify and describe the behavior of domestic emus in a farm setting. The behavioral repertoires, activity time budgets, effect of time of the day, sex, weather, and relative humidity on activities of adult emus were investigated. Eight randomly selected emus were observed using one-zero sampling method for 12 days, each period of observation lasting 30 minutes. The major behavioral categories identified were ingestive drinking, standing, locomotion, grooming, socialization, vocalization, and resting. The emus spent most of their time walking, standing, and eating. Immediately after moving to a new pen, emus were found to huddle together to keep away from emus already resident in the pen. The time spent on each activity was not significantly different between the sexes. The findings from this study provided important information on the behavior and activities of emus. The observed behaviors need to be further examined to assess their relations to the birds’ welfare.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.305
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2014
Admission routes2
Has abstractyes

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