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Record W7034375242

Transportation of animals for slaughter in Canada: Welfare issues and regulatory control

2016· article· en· W7034375242 on OpenAlexfundaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
FundersDepartment for Environment, Food and Rural Affairs, UK GovernmentEuropean CommissionMinistry of Agriculture, Food and Rural AffairsEuropean Food Safety AuthorityOntario Ministry of Agriculture, Food and Rural AffairsCanadian Food Inspection Agency
KeywordsAnimal welfareLegislationWelfareEnforcementNoticeAgency (philosophy)SubsidyControl (management)
DOInot available

Abstract

fetched live from OpenAlex

There are a number of factors involved in the transport of animals for slaughter in Canada that can potentially affect the welfare implications of transportation. These include fitness for transport, animal handling, climatic conditions (including vehicle ventilation) and journey duration. Legislation has been put in place as a means of control of industry practices to protect animal welfare during transport. This thesis examines the law relating to the protection of animal welfare during transport in Canada and assesses the impact of legislation in relation to animal welfare. The deliberations of the Canada Agricultural Review Tribunal in terms of its decisions in cases where they heard an appeal against a Notice Of Violation issued by the Canadian Food Inspection Agency for an infringement of Part XII of the Health of Animals Regulations provided one useful source of data with which to assess the effectiveness of the enforcement of the regulations for the protection of animal welfare during transport in Canada. Welfare issues when transporting poultry for slaughter are prominent. This led to a retrospective observational study of the risk factors when transporting broiler chickens for slaughter in Atlantic Canada. Results indicated that there are numerous interrelated risk factors inherent in the transport process affecting mortality during transit in Canada, with the most prominent being the weather conditions in which animals are transported. A significant interaction between the stocking density and the external temperature during transit was identified in the analysis. The mortality risk was higher in cold weather conditions compared with hot weather conditions. At very cold external temperatures, the mortality risk was reduced at high compared with low crate stocking densities, but it still remained higher than that at warmer temperatures. The stocking densities used by the slaughter plant were within the maximum recommended in the Canadian codes of practice for transport of poultry. Environmental conditions of high temperature and high humidity while at the holding barn, as indicated by an apparent equivalent temperature in the ‘high risk’ zone, resulted in a higher mortality risk than when the AET was in the medium or low risk zones. Keeping birds dry during transport results in lower mortality risk than when birds become wet. Large temperature gradients were recorded between the external temperature and that recorded within the trailer during transit and during the holding barn period, particularly in winter conditions when vehicle ventilation openings were most likely to be closed. This indicates a potential for heat stress to occur, even in winter conditions. Improvements in the monitoring and control of the thermal conditions (hot and cold) within trailers and the holding barn would be beneficial in reducing the mortality risk. During extreme weather conditions, consideration should be given to the ability of the equipment and facilities to provide appropriate conditions for the broilers when making decisions as to whether loading and transportation should be undertaken.

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 score0.721
Threshold uncertainty score0.923

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 head0.007
GPT teacher head0.177
Teacher spread0.170 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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