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Record W2912075913 · doi:10.3390/ani9020049

Housing and Management Practices on 33 Pullet Farms in Canada

2019· article· en· W2912075913 on OpenAlexafffundabout
Nienke van Staaveren, Caitlin Decina, Christine F. Baes, Tina M. Widowski, Olaf Berke, Alexandra Harlander-Matauschek

Bibliographic record

VenueAnimals · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
FundersEgg Farmers of Canada
KeywordsFlockCageLitterForagingDeep litterAgricultural scienceAnimal scienceGeographyBusinessBiologyEcologyEngineering

Abstract

fetched live from OpenAlex

Although Canada is one of the first to provide guidelines on pullet rearing in a new Code of Practice which came into effect in March 2017, little information is available about the housing and management of pullets on Canadian farms. We surveyed 99 pullet farmers and received useable responses from 33 pullet farmers (33.3% response rate) who took part in the Start Clean-Stay Clean™ program through their provincial egg boards across Canada during October⁻December 2017 as part of a larger study. Most flocks were housed in conventional cage systems (42.4%), followed by single-tier (33.3%) and multi-tier systems (24.2%). Flocks ranged from 1⁻19 weeks of age (average: 10.5 weeks of age) and were white- (58.1%) or brown-feathered (41.9%). In general, non-cage farmers met the new requirements set out in the Code of Practice for space, perches and litter provision during pullet rearing during this transitional period. Conventional caged flocks did not have opportunities for perching and foraging, but developing new methods to provide pullets with opportunities to perch and forage will become more important as the laying hen housing system transition from conventional cages to furnished cage and non-cage housing systems in Canada progresses. Additionally, clear litter management recommendations for farmers to ensure good litter quality are needed for non-cage housing systems.

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 score0.524
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.222
Teacher spread0.200 · 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

Citations9
Published2019
Admission routes3
Has abstractyes

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