MétaCan
Menu
Back to cohort
Record W2618040643

Frequency estimation of disease-causing mutations in the Belgian population of some dog breeds, part 2 : retrievers and other breed types

2016· article· en· W2618040643 on OpenAlexaboutno aff
Evy Beckers, Mario Van Poucke, L. Ronsyn, Luc Peelman

Bibliographic record

VenueGhent University Academic Bibliography (Ghent University) · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsnot available
FundersVlaamse regering
KeywordsLabrador RetrieverPopulationBreedMedicineHypoplasiaLamenessPathologyVeterinary medicineAnatomySurgeryBiologyGenetics
DOInot available

Abstract

fetched live from OpenAlex

A Belgian population of ten breeds with a low to moderately low genetic diversity or which are relatively popular in Belgium, i.e. Bichon frise, Bloodhound, Bouvier des Flandres, Boxer, Cavalier King Charles spaniel, Irish setter, Papillon, Rottweiler, Golden retriever and Labrador retriever, was genotyped for all potentially relevant disease-causing variants known at the start of the study. In this way, the frequency was estimated for 26 variants in order to improve breeding advice. Disorders with a frequency high enough to recommend routine genotyping in breeding programs are (1) degenerative myelopathy for the Bloodhound, (2) arrhythmogenic right ventricular cardiomyopathy and degenerative myelopathy for Boxers, (3) episodic falling syndrome and macrothrombocytopenia for the Cavalier King Charles spaniel, (4) progressive retinal atrophy rod cone dysplasia 4 for the Irish setter (5) Golden retriever progressive retinal atrophy 1 for the Golden retriever and (6) exercise induced collapse and progressive rod-cone degeneration for the Labrador retriever. To the authors' knowledge, in this study, the presence of a causal mutation for a short tail in the Bouvier des Flandres is described for the first time.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.007
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueGhent University Academic Bibliography (Ghent University)Same topicCardiovascular Conditions and TreatmentsFrench-language works237,207