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Record W4308114944 · doi:10.6000/1927-520x.2022.11.08

In Vitro Embryo Production in Water Buffalo

2022· article· en· W4308114944 on OpenAlexvenueno aff
Danilda Hufana‐Duran, Peregrino G. Duran

Bibliographic record

VenueJournal of Buffalo Science · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyIntracytoplasmic sperm injectionGameteEmbryoSpermIn vitro fertilisationEmbryo transferAndrologyIn vitro maturationCryopreservationIn vitroBiotechnologyOocyteCell biologyGenetics

Abstract

fetched live from OpenAlex

In vitro embryo production (IVEP) is a promising tool with many applications in producing calves from genetically superior animals desired for propagation and in the conservation and revival of endangered species. The techniques of IVEP were adopted from cattle and refined to suit the water buffalo requirements. From the collection of ovaries from a local abattoir and the collection of oocytes by ovum pick up from live animals, gamete storage, collection techniques, handling of ovaries and oocytes to keep the viability and developmental competence, selection of oocytes to the type of culture media and in vitro culture condition, and treatment of the sperm cells for in vitro fertilization are all-important components of the process that requires careful and precise action to ensure success. Trials on intracytoplasmic injection, the use of sex-sorted sperm cells as a tool for producing sex-predetermined embryos, and the somatic cell nuclear transfer are methods that can be used to produce embryos in vitro. This paper provides the important considerations involved in the production of healthy live calves out of in vitro-produced water buffalo embryos.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.245
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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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