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Record W2790421885 · doi:10.3390/ani8030042

Evaluation of Two Models of Non-Penetrating Captive Bolt Devices for On-Farm Euthanasia of Turkeys

2018· article· en· W2790421885 on OpenAlexaff
Caitlin Woolcott, Stephanie Torrey, Patricia V. Turner, Lilia Serpa, K. Schwean-Lardner, Tina M. Widowski

Bibliographic record

VenueAnimals · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsUniversity of SaskatchewanUniversity of Guelph
Fundersnot available
KeywordsPsychologyMedicine

Abstract

fetched live from OpenAlex

On-farm euthanasia is a critical welfare issue in the poultry industry and can be particularly difficult to perform on mature turkeys due to their size. We evaluated the efficacy of two commercially available non-penetrating captive bolt devices, the Zephyr-EXL and the Turkey Euthanasia Device (TED), on 253 turkeys at three stages of production: 4–5, 10, and 15–20 weeks of age. Effectiveness of each device was measured using both ante- and post-mortem measures. Application of the Zephyr-EXL resulted in a greater success rate (immediate abolishment of brainstem reflexes) compared to the TED (97.6% vs. 89.3%, p = 0.0145). Times to last movement (p = 0.102) and cardiac arrest (p = 0.164) did not differ between devices. Ante- and post-mortem measures of trauma and hemorrhage were highly correlated. Skull fractures and gross subdural hemorrhage (SDH) were present in 100% of birds euthanized with both the Zephyr-EXL and TED devices. Gross SDH scores were greater in birds killed with the Zephyr-EXL than the TED (p < 0.001). Microscopic SDH scores indicated moderate to severe hemorrhage in 92% of turkeys for the Zephyr-EXL and 96% of turkeys for the TED, with no difference between devices (p = 0.844). Overall, both devices were highly effective inducing immediate insensibility through traumatic brain injury and are reliable, single-step methods for on-farm euthanasia of turkeys.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.121
GPT teacher head0.349
Teacher spread0.229 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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