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Record W2319695777

An education and training programme for livestock transporters in Canada.

2011· article· en· W2319695777 on OpenAlexaffabout
K. S. Schwartzkopf-Genswein, Derek B. Haley, Susan Church, Jennifer Woods, Tim O'Byrne

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLivestockCertificationAnimal welfareTraining (meteorology)Production (economics)BusinessQuality (philosophy)WelfareAnimal productionBiotechnologyPolitical scienceGeographyBiologyAnimal scienceEcologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

The transport of live animals is known to be stressful and therefore can have a direct impact on animal welfare and on food safety and quality. The livestock production and transport industries are increasingly interested in improving animal well-being. This can be accomplished through the use of careful animal handling and good driving practices before, during and after transport. The recently developed Certified Livestock Transporter (CLT) training programme in Canada is aimed at ensuring livestock transporters are educated and have access to up-to-date information regarding the humane handling of animals. An overview of the CLT includes examples from the main training manual and species-specific modules. The relationship between education and improved animal welfare is discussed and possible future directions proposed. The examples provided may be modified by other users to develop new education and training programmes relevant to their geographic locations and livestock industries.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.187
Threshold uncertainty score0.376

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.002

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 head0.051
GPT teacher head0.190
Teacher spread0.140 · 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 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

Citations29
Published2011
Admission routes2
Has abstractyes

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