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Record W2143909582 · doi:10.1504/ijma.2012.048185

A cell preparation system for realising automatic zebra fish cell injection

2012· article· en· W2143909582 on OpenAlex
Cong Lü, James K. Mills

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Mechatronics and Automation · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCellProcess (computing)Computer scienceFlow cytometryNanotechnologyBiological systemBiomedical engineeringMaterials scienceChemistryBiologyEngineeringMolecular biologyBiochemistry

Abstract

fetched live from OpenAlex

Research in genetics, cancer treatment and drug therapy requires that large numbers of cells be injected with drugs or DNA to investigate cell behaviour. Traditionally, this task has been performed manually. The cell preparation stage, which includes cell separation, cell transfer, cell patterning and cell release is very time consuming. In order to increase the throughput of the cell injection process, a system that can replicate the actions of technicians is required. To prepare the cells for injection, cells must be separated and patterned without damage. This paper presents a new design to solve this problem. A cell preparation system has been designed, utilising FLOW 3D simulations, to separate and pattern cells for injection. Flow simulations of suspended cells and corresponding experiments have demonstrated that the proposed system is capable of successfully carrying out cell separation, cell patterning and cell transfer, enhancing the speed and throughput of this important laboratory technique.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

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 communication0.0000.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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