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Record W2080236079 · doi:10.1117/1.1333676

Automated single-cell sorting system based on optical trapping

2001· review· en· W2080236079 on OpenAlex

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

VenueJournal of Biomedical Optics · 2001
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsNational Research Council CanadaLaurentian UniversityUniversity of SudburyUniversity of Toronto
FundersUniversity of Toronto
KeywordsComputer scienceSortingOptical tweezersNanotechnologyOpticsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

We provide a basis for automated single-cell sorting based on optical trapping and manipulation using human peripheral blood as a model system. A counterpropagating dual-beam optical-trapping configuration is shown theoretically and experimentally to be preferred due to a greater ability to manipulate cells in three dimensions. Theoretical analysis performed by simulating the propagation of rays through the region containing an erythrocyte (red blood cell) divided into numerous elements confirms experimental results showing that a trapped erythrocyte orients with its longest axis in the direction of propagation of the beam. The single-cell sorting system includes an image-processing system using thresholding, background subtraction, and edge-enhancement algorithms, which allows for the identification of single cells. Erythrocytes have been identified and manipulated into designated volumes using the automated dual-beam trap. Potential applications of automated single-cell sorting, including the incorporation of molecular biology techniques, are discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.036
GPT teacher head0.265
Teacher spread0.229 · 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