MétaCan
Menu
Back to cohort
Record W3139366139 · doi:10.1504/ijma.2021.10036409

Automation of single cell surgery in real-time using a vision-based control system

2021· article· en· W3139366139 on OpenAlex
James K. Mills, Armin Eshaghi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Mechatronics and Automation · 2021
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlastomereAutomationComputer scienceArtificial intelligenceComputer visionMicroinjectionServo controlServoSimulationEngineeringBiologyEmbryo

Abstract

fetched live from OpenAlex

Micromanipulation of biological cells is a challenging task that requires levels of precision and repeatability which are difficult to achieve by most human operators. Automation of these processes presents an alternative approach which is capable of high precision task execution and much higher throughput, yet with its own limitations. In this paper, we propose automation methods for the image-based visual servo control feedback and tracking of both blastomeres' motion and the motion of micromanipulators, in real-time, for blastomere microinjection. An automation procedure is developed for the microinjection or blastomere biopsy of an early stage embryonic cell. These steps involve blastomere z-stack image acquisition, blastomere feature detection (x, y, z) location, and real-time image-based visual servo control of micropipettes to hold and immobilise the embryo while a micropipette injects or biopsies the blastomere. Experimental results demonstrate acceptable precision levels while performing automation procedure in real-time.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.309

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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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