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Record W4225997503 · doi:10.53730/ijhs.v6ns2.5104

Impact of age and diet on semen quality and male fertility

2022· article· en· W4225997503 on OpenAlexaff
Charles C. Mfem, S. E Oyama, O. C Otoikhila

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSemenSpermFertilitySemen analysisSemen qualityEjaculationAndrologyInfertilityMale infertilitySperm motilityBiologySperm washingPhysiologyGynecologyMedicineInseminationPopulationEndocrinologyPregnancy

Abstract

fetched live from OpenAlex

Semen analysis is comprised of a set of descriptive measurements of spermatozoa and seminal fluid parameters that help to estimate semen quality and quantity. It provides useful information concerning sperm production, sperm morphology, sperm motility and viability. The analysis also involves determination of patency of the male genital tract, secretions of the accessory organs, as well as ejaculation and emission. The purpose for semen analysis is to determine the semen constituents (both macroscopically and microscopically), determine the semen analytic parameters of men from relatively the same region, and distinguish these acquired parameters of every male subject in this project with respect to those with the possibility of infertility. (not definitive measure of fertility), and use the study to improve the knowledge of individuals interested in or affected by any form of sperm abnormalities. This is to reduce the need for more complicated interventions for the female partner, and rule out significant medical problems that may be contributing to a poor semen analysis.

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)0.002
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 score0.014
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.094
GPT teacher head0.447
Teacher spread0.353 · 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
Published2022
Admission routes1
Has abstractyes

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