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Record W4412389096 · doi:10.3390/ani15142073

Temporal Changes in Indicators of Testicular Dysgenesis Syndrome in Labrador and Golden Retrievers

2025· article· en· W4412389096 on OpenAlexaboutno aff
Thomas Lewis, Rachel Moxon, Gary England

Bibliographic record

VenueAnimals · 2025
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsnot available
Fundersnot available
KeywordsBreedHeritabilityBiologyLabrador RetrieverSemenIncidence (geometry)PhysiologyAnimal scienceGeneticsMedicinePathology

Abstract

fetched live from OpenAlex

Temporal changes in testicular traits have been reported in both humans and dogs. Analysis of % living sperm and motility from semen collections from 186 Labrador Retrievers and 113 Golden Retrievers between 2006 and 2023, and of incidents of cryptorchidism in over 15,000 dogs of the same breeds and crosses born between 1994 and 2023 was undertaken to determine influential factors. A general temporal increase in incidence of cryptorchidism masked significant differences in the trend between breeds, which persisted after accounting for genetic and litter effects. The incidence in the F1 cross was significantly lower than in either pure breed, implying hybrid vigour. The semen traits were both moderately repeatable within individuals, but this belied breed differences in its composition; for both traits, only the heritability was significantly greater than zero in the Golden Retriever, while only the permanent environment effect was present in Labrador Retrievers. There were significant negative temporal trends in Golden Retrievers for both semen traits, but not in Labrador Retrievers; significant negative effects of age (except on % motility in Labrador Retrievers); and significant negative effects of a diagnosis of benign prostatic hyperplasia on both traits in both breeds. These results reveal complex breed by environment interactions in traits related to testicular form and function.

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 score0.002
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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