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Record W4411375293 · doi:10.1063/5.0238824

Line-scan imaging for real-time phenotypic screening of <i>C. elegans</i>

2025· article· en· W4411375293 on OpenAlexafffund
Aaron Au, Maximiliano Giuliani, Sean Harrington, Peter J. Roy, Christopher M. Yip

Bibliographic record

VenueReview of Scientific Instruments · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research - Antimicrobial Resistance Research InitiativeNatural Sciences and Engineering Research Council of Canada
KeywordsImage stitchingField of viewLine (geometry)Image resolutionOpticsComputer scienceComputer visionField (mathematics)Direct imagingArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Phenotypic screens of living organisms often require real-time, high spatial resolution imaging. We report here on the design and application of a line-scan imaging platform that enables bright-field imaging of live nematodes using a 96-well plate configuration. The system optics are such that each individual well is projected across the full field of view of a 4 K line scanning camera. To minimize motional artifacts, the camera system is translated, while the sample is kept stationary. We discuss the challenges in establishing flat field illumination, image registration and stitching, and the opportunities that this platform has enabled for phenotypic screens of living systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations1
Published2025
Admission routes2
Has abstractyes

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