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Record W2340252305 · doi:10.3389/fchem.2016.00017

Improved Tracking and Resolution of Bacteria in Holographic Microscopy Using Dye and Fluorescent Protein Labeling

2016· article· en· W2340252305 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

VenueFrontiers in Chemistry · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDigital Holography and Microscopy
Canadian institutionsMcGill University
FundersJet Propulsion LaboratoryMcGill UniversityCalifornia Institute of TechnologyKeck Institute for Space StudiesGordon and Betty Moore Foundation
KeywordsFluorescenceMicroscopyFluorescence microscopeBacteriaResolution (logic)Fluorescent proteinFluorescent labellingHolographyBiophysicsChemistryTracking (education)Materials scienceGreen fluorescent proteinNanotechnologyChromatographyOpticsBiologyBiochemistryComputer sciencePhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Digital holographic microscopy (DHM) is an emerging imaging technique that permits instantaneous capture of a relatively large sample volume. However, large volumes usually come at the expense of lower spatial resolution, and the technique has rarely been used with prokaryotic cells due to their small size and low contrast. In this paper we demonstrate the use of a Mach-Zehnder dual-beam instrument for imaging of labeled and unlabeled bacteria and microalgae. Spatial resolution of 0.3 μm is achieved, providing a sampling of several pixels across a typical prokaryotic cell. Both cellular motility and morphology are readily recorded. The use of dyes provides both amplitude and phase contrast improvement and is of use to identify cells in dense samples.

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 score0.213
Threshold uncertainty score0.419

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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