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Record W3036006600 · doi:10.3390/ani10061051

Determination of Dairy Cattle Euthanasia Criteria and Analysis of Barriers to Humane Euthanasia in the United States: The Veterinarian Perspective

2020· article· en· W3036006600 on OpenAlexaff
B.K. Wagner, Catie Cramer, Heather Fowler, Hannah L. Varnell, Alia M. Dietsch, Kathryn L. Proudfoot, Jan K. Shearer, Maria Correa, Monique Pairis‐Garcia

Bibliographic record

VenueAnimals · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Prince Edward Island
FundersU.S. Department of Agriculture
KeywordsPerspective (graphical)Environmental ethicsPhilosophyMathematics

Abstract

fetched live from OpenAlex

When dairy cattle become ill or injured to the extent that recovery is unlikely or impossible, on-farm euthanasia should be used as a tool to eliminate pain and suffering. Our study aimed to identify decision-making criteria and the most common factors considered by veterinarians when making and carrying out euthanasia decisions. Dairy cattle veterinarians were recruited to participate in an online survey (Part I, 61 surveys collected) or in one of three focus groups (Part II, 4–10 veterinarians/group, n = 22). Part I (survey): Surveyed veterinarians varied regarding health condition management and demonstrated a strong proclivity to treat compromised cattle, mirroring trends amongst dairy producers identified in previous research. Sixty percent of respondents indicated that most facilities for which they serve as the primary veterinarian have a written euthanasia protocol in place. Part II (focus groups): Three main themes about euthanasia decision-making (logistical, animal, and human) were identified from focus group discussions. Discussions focused primarily on logistical factors such as financial considerations and client/public perceptions. Development of specific standards for euthanasia, alongside interactive training programs for dairy veterinarians and producers are vital next steps to improving cattle welfare and consistency in euthanasia decision-making across the United States dairy industry.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.270

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 head0.061
GPT teacher head0.393
Teacher spread0.332 · 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 designBench or experimental
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

Citations24
Published2020
Admission routes1
Has abstractyes

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