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Record W4317786967 · doi:10.1016/j.ohx.2023.e00399

Fabrication and validation of an LED array microscope for multimodal, quantitative imaging

2023· article· en· W4317786967 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHardwareX · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDigital Holography and Microscopy
Canadian institutionsnot available
FundersNational Institutes of HealthMcMaster UniversityNational Cancer InstituteMelanoma Research Alliance
KeywordsMicroscopeModular designComputer scienceSoftwareRoboticsPython (programming language)Artificial intelligenceComputer hardwareBiomedical engineeringOpticsEngineeringPhysicsRobot

Abstract

fetched live from OpenAlex

The combination of multiple imaging modalities in a single microscopy system can enable new insights into biological processes. In this work, we describe the construction and rigorous characterization of a custom microscope with multimodal imaging in a single, cost-effective system. Our design utilizes advances in LED technology, robotics, and open-source software, along with existing optical components and precision optomechanical parts to offer a modular and versatile design. This microscope is operated using software written in Arduino and Python and has the ability to run multi-day automated imaging experiments when placed inside of a cell culture incubator. Additionally, we provide and demonstrate methods to validate images taken in brightfield and darkfield, along with validation and optimization for differential phase contrast (DPC) quantitative phase imaging.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.298

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 head0.017
GPT teacher head0.310
Teacher spread0.292 · 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