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Record W2773438101

Environmental quality and animal welfare implications of commercial livestock transportation to slaughter facilities in North America: a review

2017· report· en· W2773438101 on OpenAlexaboutno aff
Sarah J. Schuetze

Bibliographic record

VenueK-State Research Exchange (Kansas State University) · 2017
Typereport
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsLivestockAnimal welfareBusinessWelfareQuality (philosophy)Agricultural economicsNatural resource economicsEnvironmental planningGeographyEconomicsEcologyBiologyForestry
DOInot available

Abstract

fetched live from OpenAlex

There are several stressful events throughout an animal's lifetime, but transportation is considered one of the most detrimental events to animal welfare by many professionals, regardless of species.Transportation consists of several different interacting and compounding factors that can affect animal welfare and meat product quality.The purpose of this report is to review current industry practices of land transport of different livestock types to slaughter facilities, primarily within the United States and Canada.This review evaluated species-specific transport practices and subsequent effects on animal welfare and carcass quality for both animal welfare and economic outlooks.Regulations are placed on the driver and time limits that the animals are allowed to be in transit.Trailer style use partially depends on the age and species of animal that is being hauled.I acknowledge, with gratitude, the support and encouragement from my committee members.Dr. Maghirang has been an excellent source of guidance and motivation throughout my undergraduate and graduate degrees.His patience and mentorship have helped me flourish and his shared knowledge will never be forgotten.I appreciate Dr. Thomson and the invaluable industry exposure I received under his direction.These experiences shaped me as an individual and as a researcher, and will be something I carry with me into future industrial opportunities.Dr. Liu is always willing to help and encourages deeper thought processes, regardless of the topic, for which I am very incredibly grateful.I wish to thank the Biological and Agricultural Engineering Department.There are so many individuals who were always available with encouragement and advice.This has been my niche and my home away from home for over half a decade and it has become a part of who I am.I would also like to thank the College of Veterinary Medicine for allowing me to become an adopted member for the past few years while completing my graduate experience.I would like to thank my sister, Rachel, who has supported me through everything.I am forever grateful to my mother, Carol

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.179
GPT teacher head0.355
Teacher spread0.175 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2017
Admission routes1
Has abstractyes

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