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Record W2090828103 · doi:10.1364/ol.37.002151

X-ray in-line phase tomography of multimaterial objects

2012· article· en· W2090828103 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.

Bibliographic record

VenueOptics Letters · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsCanadian Nautical Research Society
Fundersnot available
KeywordsProjection (relational algebra)A priori and a posterioriTomographyObject (grammar)Computer scienceTomographic reconstructionDomain (mathematical analysis)AttenuationPhase (matter)Line (geometry)Radon transformPhase retrievalComputer visionDiffraction tomographyHomogeneousDiffractionArtificial intelligenceIterative reconstructionOpticsAlgorithmPhysicsMathematicsGeometryStatistical physics

Abstract

fetched live from OpenAlex

We present a method for phase retrieval from x-ray Fresnel diffraction patterns for multimaterial objects. Previously, homogeneous object assumptions have been used and have been introduced in the Radon domain. Here, we apply prior knowledge in the object domain, which permits the introduction of multiple materials. This is achieved first by a tomographic reconstruction of an attenuation scan and then introduction of the prior followed by a forward projection step to yield the a priori phase maps. The method is applied to the reconstruction of an object of known composition consisting of both soft and hard materials and is shown to perform better than previously proposed algorithms.

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 score0.052
Threshold uncertainty score0.518

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 head0.012
GPT teacher head0.296
Teacher spread0.285 · 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