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Record W2197408686 · doi:10.1063/1.4935983

A laboratory based edge-illumination x-ray phase-contrast imaging setup with two-directional sensitivity

2015· article· en· W2197408686 on OpenAlexaff
Gibril K. Kallon, Michal J. Wesolowski, Fabio A. Vittoria, Marco Endrizzi, Dario Basta, Thomas P. Millard, Paul C. Diémoz, Alessandro Olivo

Bibliographic record

VenueApplied Physics Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsUniversity of Saskatchewan
FundersScience and Technology Facilities CouncilEngineering and Physical Sciences Research CouncilSeventh Framework ProgrammeBurroughs Wellcome Fund
KeywordsSensitivity (control systems)Phase (matter)OpticsPhase-contrast imagingX-Ray Phase-Contrast ImagingStreakContrast (vision)Differential phasePhase retrievalPhase contrast microscopyPhysicsDifferential (mechanical device)Computer scienceFourier transformElectronic engineering

Abstract

fetched live from OpenAlex

We report on a preliminary laboratory based x-ray phase-contrast imaging system capable of achieving two-directional phase sensitivity, thanks to the use of L-shaped apertures. We show that in addition to apparent absorption, two-directional differential phase images of an object can be quantitatively retrieved by using only three input images. We also verify that knowledge of the phase derivatives along both directions allows for straightforward phase integration with no streak artefacts, a known problem common to all differential phase techniques. In addition, an analytical method for 2-directional dark field retrieval is proposed and experimentally demonstrated.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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
GenreEmpirical

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

Citations31
Published2015
Admission routes1
Has abstractyes

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