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Record W368831072

Nutritional Influences on Horn Quality and Hoof Health

2009· article· en· W368831072 on OpenAlexaff
Christoph Muelling

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsClawLamenessCullingMedicineFoot rotHoofLaminitisEnvironmental healthVeterinary medicineBiotechnologyBiologySurgeryHerdEcology
DOInot available

Abstract

fetched live from OpenAlex

8 Clinical lesions of the cattle hoof are the result of or at least associated with poor hoof horn quality. 8 Formation of claw horn is a complex process which essentially depends on a balanced diet and a sufficient supply with vitamins, minerals and trace elements. 8 We have evidence for positive effects of biotin and zinc on horn quality and hoof health. 8 Nutritional influences on horn quality can be used to improve integrity of horn and thus reduce the incidence of claw problems and lameness in dairy herds Lameness in dairy cows is a major cause of suffering and economic loss with up to 52 % (average 20-25%) of cows becoming lame each year (Clarkson et al., 1996; Green et al., 2002). The vast majority of lameness cases are caused by a variety of non-infectious and infectious claw diseases causing

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.157
GPT teacher head0.442
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2009
Admission routes1
Has abstractyes

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