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Techniques for the Insemination of Low Doses of Stallion Sperm

2010· review· en· W1580321548 on OpenAlexaff
J.C. Samper, Tracy A. Plough

Bibliographic record

VenueReproduction in Domestic Animals · 2010
Typereview
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsLangley Environmental Partners Society
Fundersnot available
KeywordsInseminationSpermSemenSperm washingArtificial inseminationFertilityGynecologyAndrologyBiologyMedicinePregnancyPopulation

Abstract

fetched live from OpenAlex

CONTENTS: In the last decade, there has been a significant increase in the quality and commercial use of frozen equine semen. The emergence of new reproductive technologies, coupled with the high prices for an insemination dose from some stallions, the increasing costs of import and export and the marketing policies of stallion agents or owners in the sport horse industry has stimulated the fractionation of doses for insemination. Consequently, the sperm number and the volume of an insemination dose are significantly reduced. To deliver lower doses of sperm in lower volumes compared to the standard dose, two techniques are used in clinical practice. Semen can be delivered hysteroscopically (HI) or by rectally guiding a flexible pipette to the tip of the desired uterine horn (RI). Both techniques have been described with good success and have triggered an incentive to further reduce the number of spermatozoa without having a negative effect on fertility. This article will review the expected success of both techniques in clinical settings and will highlight their advantages and disadvantages both for the mare and stallion. In addition, some of the implications of reducing sperm numbers on the industry will be discussed. From the available information, it is evident that lower sperm numbers deposited by RI or HI to deliver the inseminate can result in acceptable pregnancy rates with fresh or frozen semen in commercial settings. These methods of insemination could have major implications in the implementation and commercialization of new and emerging technologies in the equine industry.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.046
GPT teacher head0.376
Teacher spread0.330 · 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 designOther design
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

Citations25
Published2010
Admission routes1
Has abstractyes

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