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Record W3189740744 · doi:10.21423/aabppro20208050

What we know that just ain’t so

2020· article· en· W3189740744 on OpenAlexafffund
K. S. Schwartzkopf-Genswein, Sònia Martí, E. D. Janzen, Daniela M Meléndez

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of CalgaryAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaBeef Cattle Research Council
KeywordsMeloxicamCastrationLivestockBeef industryPain managementMedicinePain assessmentAnimal welfareBusinessPhysical therapyAgricultural scienceGeographyInternal medicineBiology

Abstract

fetched live from OpenAlex

It is well documented that castration, regardless of age or method (surgical or band) causes pain in beef cattle. At the same time, consumer concern and awareness regarding painful routine management procedures in livestock is at an all-time high. Developing pain mitigation strategies that are practical and cost effective for producers is also important to facilitate their adoption by the beef industry. The goal of this paper is to review current pain assessment and mitigation strategies for knife and band castration in pre-weaned calves with focus on the influence of the age and method of castration, including a summary of recent studies assessing the effects of meloxicam in controlling calf pain and improving calf comfort.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.273
Teacher spread0.219 · 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 designNot applicable
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

Citations0
Published2020
Admission routes2
Has abstractyes

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