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Record W2919423303 · doi:10.1364/oe.27.007787

Three-dimensional focal stack imaging in scanning transmission X-ray microscopy with an improved reconstruction algorithm

2019· article· en· W2919423303 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

VenueOptics Express · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaMinistry of Science and Technology of the People's Republic of ChinaCanadian Light Source
KeywordsOpticsStack (abstract data type)MicroscopyTransmission (telecommunications)AlgorithmMaterials scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

Focal stack (FS) is an effective technique for fast 3D imaging in high-resolution scanning transmission X-ray microscopy. Its crucial issue is to assign each object within the sample to the correct position along the optical axis according to a proper focus measure. There is probably information loss with previous algorithms for FS reconstruction because the old algorithms can only detect one focused object along each optical-axial pixel line (OAPL). In this study, we present an improved FS algorithm, which utilizes an elaborately calculated threshold for normalized local variances to extract multiple focused objects in each OAPL. Simulation and experimental results show its feasibility and high efficiency for 3D imaging of high contrast, sparse samples. It is expected that our advanced approach has potential applications in 3D X-ray microscopy for more complex 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.804
Threshold uncertainty score0.856

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 communication0.0000.001
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 head0.006
GPT teacher head0.254
Teacher spread0.248 · 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