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Ultrasonography for Monitoring Reproductive Function in the Bull

2012· review· en· W1948732377 on OpenAlexafffund
John P. Kastelic, Leonardo F.C. Brito

Bibliographic record

VenueReproduction in Domestic Animals · 2012
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsEchogenicityScrotumSemen qualitySemenUltrasonographySpermSemen analysisBiologyTesticleMedicinePhysiologyGynecologyAnatomyAndrologyRadiologyInfertilityPregnancy

Abstract

fetched live from OpenAlex

Diagnostic ultrasonography has been widely used for examination of the reproductive tract of female cattle, but more sparingly in bulls. Typical clinical ultrasonographic examinations of bull testes are unlikely to affect semen quality or sperm production. The ultrasonographic anatomy of bull testes and accessory sex glands has been reported. Although testicular echogenicity increased (i.e. the parenchyma appeared more white) as a bull approached puberty, echogenicity was not superior to scrotal circumference as a predictor of puberty. Ultrasonography can be used to detect and characterize testicular pathology. It is noteworthy that areas of increased echogenicity (testicular fibrosis) are common, especially in young bulls, but are not associated with decreased semen quality (e.g. percentage of morphologically abnormal sperm). Neither visual evaluation nor computerized pixel analysis of testicular ultrasonic echotexture was consistently predictive of semen quality in bulls. Therefore, we concluded that the primary clinical use of ultrasonography in assessment of reproductive function in the bull is characterization of grossly detectable lesions in the testes and scrotum.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.101
GPT teacher head0.339
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations49
Published2012
Admission routes2
Has abstractyes

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