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Record W2743035440 · doi:10.1109/marss.2017.8001898

Study of robotic system for automated oocyte manipulation

2017· article· en· W2743035440 on OpenAlexaff
Junhui Zhu, Longcheng Gao, Peng Pan, Yong Wang, Ruihua Chen, Changhai Ru

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsOocyteContainer (type theory)Computer scienceComputer visionRepeatabilityArtificial intelligenceSimulationEmbryoEngineeringBiologyCell biologyChemistry

Abstract

fetched live from OpenAlex

This paper reports a robotic micromanipulation system with a lab-developed cell container to handle oocyte or embryo automatically. The cell container can be an alternative to assemblage of multiple culture-dishes. Based on microscopic image processing and motion control algorithms, cells were tracked in real time and relational moving components were synchronously controlled during the whole procedure. Vitrification for oocyte (embryo) was conducted at a speed of 25-35 seconds: no more than 2 seconds for cell aspiration and 3-4 seconds for cell transfer. Experimental results demonstrated that automated oocyte manipulation was of high repeatability because of few cell missing from computer vision; and its success rate was 89.6%, which is higher than manual operation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.196

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)0.0000.000

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 head0.044
GPT teacher head0.267
Teacher spread0.223 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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