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Record W1974167096 · doi:10.3138/jvme.37.1.22

An Australian Perspective on Developing Standards and Ensuring Compliance

2010· article· en· W1974167096 on OpenAlexvenueno aff
P. M. Thornber

Bibliographic record

VenueJournal of Veterinary Medical Education · 2010
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareLegislationEnforcementLivestockBusinessWelfareBlueprintPolitical sciencePublic relationsEngineeringLaw

Abstract

fetched live from OpenAlex

Australia is a federation of states and territories, each with their own parliament and animal-welfare laws. Australian animal-welfare legislation imposes a "duty of care" on people responsible for the care and well-being of animals under their management. In the livestock sector, this responsibility is mirrored by the ongoing development of standards, guidelines, and codes of practice to assist people to understand their responsibilities and to meet expectations concerning animal welfare. The Australian Animal Welfare Strategy (AAWS) is the national animal-welfare policy blueprint for sustainable improvements in animal welfare, and one of its key goals is to achieve greater consistency in the development, implementation, and enforcement of animal-welfare standards. Standards, guidelines, and model codes also inform the development of contemporary, evidence-based quality assurance programs for individual livestock industries and provide the basis for competency-based training programs for animal handlers. Australian standards have been developed for pigs and land transport of livestock, and work is progressing on national standards for cattle, sheep, horses, zoo animals, dogs, and cats. Other achievements include the development of requirements for the care and use of animals in research and teaching, guidelines for the welfare of aquatic animals, and codes of practice for the humane killing of pest animals. State and territory governments are developing a framework for consistent regulation and compliance in consultation with industries and welfare organizations.

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)0.150
metaresearch head score (Gemma)0.148
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: none
Teacher disagreement score0.150
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1500.148
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.006
Science and technology studies0.0110.019
Scholarly communication0.0150.015
Open science0.0070.015
Research integrity0.0180.017
Insufficient payload (model declined to judge)0.0070.001

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 head0.165
GPT teacher head0.493
Teacher spread0.328 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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