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Record W4403830471 · doi:10.1080/00071668.2024.2414460

Does the distribution of light intensity within the barn impact broiler production and welfare?

2024· article· en· W4403830471 on OpenAlexaff
T. Shynkaruk, M. L. Parsons, Carolin Adler, C. Goeree, Kathleen Long, K. Schwean-Lardner

Bibliographic record

VenueBritish Poultry Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsMaple Leaf FoodsUniversity of Saskatchewan
Fundersnot available
KeywordsBroilerBarnWelfareIntensity (physics)Production (economics)Distribution (mathematics)Animal scienceLight intensityAnimal welfareEnvironmental scienceBiologyEconomicsMathematicsGeographyPhysicsEcologyOpticsMicroeconomicsMarket economy

Abstract

fetched live from OpenAlex

1. The objective of this study was to determine if rearing broilers under variable light intensity (VLI) impacted their welfare or productivity.2. Ross 308 broilers (n = 7,256) were reared until 35 d of age and exposed to a uniform intensity of 10 lux (CON) or VLI with low intensity areas of 2–5 lux proximal to the walls and high intensity areas of 84–133 lux proximal to feeders.3. The data were analysed as a complete randomised design using an analysis of variance. Significance was declared when p ≤ 0.05.4. Applying VLI resulted in increased feed intake early in life but had no impact on body weight. Overall efficiency was improved in the CON treatment. Mortality diagnoses of skeletal problems were reduced under VLI. Treatment had no impact on footpad, hock or gait score, heterophil to lymphocyte ratio or melatonin concentration. Birds performed certain behaviours in specific locations within the room, independent of light intensity treatment.5. In conclusion, raising broilers under VLI had little impact on production or most welfare parameters assessed in this study. However, satisfying the bird’s preference for different light intensities may improve 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 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 score0.961
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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