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Record W2906893325 · doi:10.18548/aspe/0006.07

LAPAROSCOPIC OVUM PICK-UP (LOPU): FROM ANIMAL PRODUCTION TO CONSERVATION

2018· article· en· W2906893325 on OpenAlexaff
Letícia Alecho Requena, Cristiane Schilbach Pizzutto, Hernán Baldassarre

Bibliographic record

VenueSPERMOVA · 2018
Typearticle
Languageen
FieldMedicine
TopicCongenital Anomalies and Fetal Surgery
Canadian institutionsMcGill University
Fundersnot available
KeywordsProduction (economics)Blood conservationAnimal productionBiologyAnimal scienceEconomics

Abstract

fetched live from OpenAlex

Laparoscopic Ovum Pick-Up (LOPU) is the most reliable and efficient technique for collecting high quality oocytes from live animals in certain species or age groups, allowing its use for In Vitro Embryo Production (IVEP) and Somatic Cell Nuclear Transfer (SCNT). In order to maximize the number and quality of oocytes collected by donor, it is necessary to synchronize estrus and stimulate follicular growth using hormonal protocols that vary according to species. There are 2 big categories of applications for the LOPU-IVEP technology in production animals, those in which it acts as an alternative to MOET (competitive applications) and those in which it doesn't compete with MOET as it cannot be done in those categories (non competitive applications). In wild animals, LOPU can play an important role in conservation programs for endangered species when associated with effective IVEP and has been done in several species. It has commercial application in sheep, goats, cattle and buffaloes calves. Repeating LOPU procedures in the same female does not cause sequels with impact on the female's reproductive life, even when performed on prepubertal or wild animals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.025
GPT teacher head0.273
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2018
Admission routes1
Has abstractyes

Explore more

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