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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.Cattle are more likely to be hauled in pot belly trailers, while pigs are often transported in either forever grateful to my mother, Carol, for her endless encouragement and my father, Chris, who was never too busy to be my sounding board.In addition, I would like thank Tyler for making me put things in perspective.Also, a huge "Thank you" to all my family, friends, peers, and professors who have encouraged me to stay focused as I completed this graduate experience.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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