MétaCan
Menu
Back to cohort
Record W2035972471 · doi:10.1063/1.4913271

Open-frame system for single-molecule microscopy

2015· article· en· W2035972471 on OpenAlexafffund
Adriel Arsenault, Jason S. Leith, Gil Henkin, Christopher McFaul, Matthew S. Tarling, Richard Talbot, Daniel Berard, François Michaud, Shane Scott, Sabrina Leslie

Bibliographic record

VenueReview of Scientific Instruments · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsMcGill University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaDivision of Materials ResearchCanada Foundation for Innovation
KeywordsChassisMicroscopeFlexibility (engineering)MicroscopyNanotechnologyLens (geology)Modular designMaterials scienceComputer scienceImage stitchingOpticsComputer visionPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

We present the design and construction of a versatile, open frame inverted microscope system for wide-field fluorescence and single molecule imaging. The microscope chassis and modular design allow for customization, expansion, and experimental flexibility. We present two components which are included with the microscope which extend its basic capabilities and together create a powerful microscopy system: A Convex Lens-induced Confinement device provides the system with single-molecule imaging capabilities, and a two-color imaging system provides the option of imaging multiple molecular species simultaneously. The flexibility of the open-framed chassis combined with accessible single-molecule, multi-species imaging technology supports a wide range of new measurements in the health, nanotechnology, and materials science research sectors.

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)0.000
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.007

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 head0.033
GPT teacher head0.344
Teacher spread0.311 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueReview of Scientific InstrumentsSame topicAdvanced Fluorescence Microscopy TechniquesFrench-language works237,207