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Record W2274975129 · doi:10.1016/j.meatsci.2016.02.033

A note comparing the welfare of Zebu cattle following three stunning-slaughter methods

2016· article· en· W2274975129 on OpenAlex
Julia Eumira Gomes Neves, Mateus José Rodrigues Paranhos da Costa, Roberto de Oliveira Roça, L. Faucitano, N.G. Gregory

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMeat Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsAgriculture and Agri-Food Canada
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsStunningZebuAnimal scienceAnimal welfareIce waterMedicineMyocardial stunningVeterinary medicineBiologyInternal medicineFood science

Abstract

fetched live from OpenAlex

The objective of this study was to assess welfare of cattle during bleeding after slaughter with or without stunning. A total of 434 bulls were distributed across three slaughter treatments: penetrating captive bolt stunning followed by chest sticking (PCB, N=279), non-penetrating captive bolt stunning followed by halal slaughter (NPCB, N=67) and shechita without previous stunning (SHE, N=88). Four measures of possible consciousness and return to sensibility were recorded 20 and 60 s after bleeding as welfare indicators. They were the frequencies of responses to nostril stimulation and tongue pinch, spontaneous eye blinking, and rhythmic breathing. All responses were absent in stunned cattle at both 20 and 60 s, and in SHE cattle 7, 4, 10, and 100% of the animals presented these responses, respectively. Repeat shots were required for 46% NPCB and 2% PCB (P<0.05). The application of religious slaughter without previous stunning may result in greater risk of cattle suffering, pain and distress at slaughter.

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.

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)0.000
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 score0.675
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.085
GPT teacher head0.339
Teacher spread0.254 · 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