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Record W4213327871 · doi:10.1017/9781009181648.009

Sperm Preparation

2022· book-chapter· en· W4213327871 on OpenAlexaff
David Mortimer, Lars Björndahl, Christopher L. R. Barratt, José Antonio Castilla, Roelof Menkveld, Ulrik Kvist, Juan G. Álvarez, Trine B. Haugen

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook-chapter
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsAchieve Life Sciences (Canada)
Fundersnot available
KeywordsSemenSpermStandardizationRobustness (evolution)Computer scienceBiomedical engineeringBiologyAndrologyMedicine

Abstract

fetched live from OpenAlex

The physiology and basic principles of sperm washing and other preparative techniques are described, including the limitations and even dangers posed by some methods. Optimized methods for sperm preparation using direct swim-up from semen and density gradient centrifugation are provided as standard operating procedures (SOPs) for easy use at the bench. Methods are focussed on standardization and robustness, and minimizing the risk iatrogenic damage to the spermatozoa and avoiding errors. There are sections on selecting which method to use, dealing with atypical semen specimens, processing multiple specimens, and potential new technology for preparing human spermatozoa for use in assisted conception treatment.

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: Other · Consensus signal: Other
Teacher disagreement score0.093
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0930.077

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 head0.024
GPT teacher head0.217
Teacher spread0.193 · 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
GenreOther

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

Citations1
Published2022
Admission routes1
Has abstractyes

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