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Record W2024703147 · doi:10.4141/cjas09068

An audit of transport conditions and arrival status of slaughter cattle shipped by road at an Ontario processor

2010· article· en· W2024703147 on OpenAlexaffvenueabout
Laura Warren, I. B. Mandell, K G Bateman

Bibliographic record

VenueCanadian Journal of Animal Science · 2010
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTruckAllowance (engineering)Beef cattleAnimal welfareAnimal sciencePopulationEnvironmental scienceTransport engineeringGeographyEngineeringEnvironmental healthBiologyOperations managementMedicineEcology

Abstract

fetched live from OpenAlex

This is an observational study to investigate slaughter cattle transportation conditions in Canada. Data collected include: length of time in transit; temperature variation; season; weather transport conditions; cattle weight; sex and whether sexes were separated on mixed loads; number of lots and whether lots were separated; cattle unloading speed; cattle handling score; trucker training and experience hauling cattle; ventilation; and condition of cattle at arrival. Information was collected on approximately 50 000 animals transported by 1363 trucks. All but 0.2% of trucks arrived within the 52 h allowable transport time before unloading required for rest, feed, and water. Most trucks (85.7%) were from within 8 h of the plant. Trucks surveyed were at or above the recommended space allowance 49% of the time. There were five non-ambulatory (unable to walk off the truck with or without assistance) or dead, 79 lame, and four animals that needed assistance of the 49 959 animals observed (0.4, 4.8 and 0.2%, respectively, of the trucks surveyed). However, these concerns were not necessarily a result of transportation, as animal health at loading was unknown. There were very few visible animal welfare concerns associated with the transportation of slaughter cattle in the population sampled. Key words: Cattle, transport, welfare, beef

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 score0.594
Threshold uncertainty score0.895

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.311
Teacher spread0.280 · 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

Citations17
Published2010
Admission routes3
Has abstractyes

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