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Record W3116694061 · doi:10.3390/ani11010017

Assessing Environmental Control Strategies in Cage-Free Egg Production Systems: Effect on Spatial Occupancy and Natural Behaviors

2020· article· en· W3116694061 on OpenAlexafffund
Andrés F. Gonzalez-Mora, Araceli D. Larios, Alain N. Rousseau, Stéphane Godbout, Cédric Morin, Joahnn H. Palacios, Michèle Grenier, Sébastien Fournel

Bibliographic record

VenueAnimals · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversité LavalInstitut de Recherche et de Développement en AgroenvironnementInstitut National de la Recherche Scientifique
FundersMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsLitterAnimal welfareCageAllowance (engineering)Natural experimentProduction (economics)LayingControl (management)Environmental healthEnvironmental scienceAnimal scienceToxicologyComputer scienceStatisticsOperations managementMathematicsEcologyMedicineBiologyEngineeringEconomics

Abstract

fetched live from OpenAlex

Animal welfare concerns have been a challenging issue for producers and international marketing. In laying hen production, cage-free systems (CFS) have been identified as an alternative to ensure the laying hens’ well-being. Nevertheless, in CFS, important environmental issues have been reported, decreasing indoor air quality. Environmental control strategies (ECS) have been designed to enhance indoor air quality in CFSs. However, little information exists about the effect of these ECSs on natural animal behaviors. Four strategies and one control were tested in an experimental CFS, previously designed to track behavioral variables using video recordings over seven time-lapses of 1 hour per day. Spatial occupancy (SO) and laying hen behaviors (LHB) were registered. One statistical analysis was applied to evaluate the effect of ECS on SO and LHB using a multinomial response model. Results show lower chances to use litter area within the reduction of litter allowance treatment (T17) (p < 0.05). Neither the four ECSs nor the control implemented in this experiment affected the natural behaviors of the hens. However, stress patterns and high activity were reported in the T17 treatment. This study shows that it is possible to use these ECSs without disrupting laying hens’ natural behaviors.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.889

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 head0.032
GPT teacher head0.319
Teacher spread0.287 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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