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Record W2019155950 · doi:10.3390/ani4010062

Characteristics of Loads of Cattle Stopping for Feed, Water and Rest during Long-Distance Transport in Canada

2014· article· en· W2019155950 on OpenAlexafffundabout
Hannah E. Flint, K. S. Schwartzkopf-Genswein, K G Bateman, Derek B. Haley

Bibliographic record

VenueAnimals · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersOntario Ministry of Food and AgricultureCanadian Food Inspection Agency
KeywordsStockingRest (music)Animal scienceEnvironmental scienceTruckTrailerBiologyEngineeringMedicine

Abstract

fetched live from OpenAlex

This study is the first comprehensive examination of long-haul cattle being transported across Canada and off-loaded for feed, water and rest. A total of 129 truckloads were observed at one of two commercial rest stations near Thunder Bay, Ontario. Data collected included information regarding the truck driver, the trailer, the trip, the animals and animal handling. The majority of the loads stopping were feeder calves (60.94%) while 21.09% were weaned calves, and the remaining 14.84% were market weight cattle. The truck loads surveyed were in transit for, on average, 28.2 ± 5.0 hours before stopping and cattle were rested for an average of 11.2 ± 2.8 hours. These data suggest that loads stopping at the rest station were adhering to the regulations stated in the Health of Animals Act, which outline a maximum of 48 hours in transit before a mandatory stop of at least 5 hours for feed, water and rest. There was a large amount of variability around how well recommendations, such as stocking density were followed. Further research is required to assess how well cattle are coping with long-distance transport under current regulations and industry practices.

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 score0.576
Threshold uncertainty score0.873

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 head0.009
GPT teacher head0.181
Teacher spread0.172 · 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

Citations9
Published2014
Admission routes3
Has abstractyes

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