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Record W2145231208 · doi:10.3138/jvme.37.1.94

Galloping Colts, Fetal Feelings, and Reassuring Regulations: Putting Animal-Welfare Science into Practice

2010· article· en· W2145231208 on OpenAlexvenueno aff
David Mellor

Bibliographic record

VenueJournal of Veterinary Medical Education · 2010
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareFeelingWelfareEthologyPsychologyLivestockFetusDevelopmental psychologyMedicineVeterinary medicinePolitical sciencePregnancySocial psychologyBiologyLaw

Abstract

fetched live from OpenAlex

About a decade ago, concern was expressed that fetuses might suffer while dying in utero after the death of their dams. However, reference to already published literature provided compelling evidence that fetuses cannot consciously experience negative sensations or feelings, such as breathlessness and pain, and showed that, provided certain precautions are taken, they cannot suffer--their welfare is assured. In this article, I outline the major features of fetal and neonatal physiology that underlie this conclusion as it relates to fetuses that are neurologically exceptionally immature, moderately immature, or mature at birth. As an example of the practical application of this knowledge, I also show how the results of detailed studies reported in the biomedical literature, together with evolving understanding of the capacity of animals to experience negative sensations reported in the animal-welfare science literature, led to the development of international guidelines for the humane management of livestock fetuses when their dams are slaughtered commercially. I also highlight the notion that significant progress in the scientific understanding of animal welfare, and its applications, can be made by remaining open to knowledge developed in disciplines at the margins of or beyond those in the animal-welfare science, ethology, and veterinary sciences arenas.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.839
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.419
Teacher spread0.364 · 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

Citations64
Published2010
Admission routes1
Has abstractyes

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