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Record W2141535361 · doi:10.1109/jproc.2002.1002529

Direct-conversion flat-panel X-ray image sensors for digital radiography

2002· article· en· W2141535361 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

VenueProceedings of the IEEE · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsUniversity of TorontoUniversity of Saskatchewan
Fundersnot available
KeywordsDetective quantum efficiencyFlat panel detectorFlat panelX-ray detectorDetectorDigital radiographyQuantum efficiencyOpticsX-rayRadiographyImage qualityActive matrixSensitivity (control systems)OptoelectronicsNoise (video)PhysicsComputer scienceImage (mathematics)Electronic engineeringComputer visionEngineering

Abstract

fetched live from OpenAlex

Advances in active-matrix array flat panels for displays over the last decade have led to the development of flat-panel X-ray image detectors. Recent flat-panel detectors have shown image quality exceeding that of X-ray film/screen cassettes. They can also permit the instantaneous capture, readout, and display of digital X-ray images and, hence, enable the clinical transition to digital radiography. There are two general approaches to flat panel detector technology: 1) direct and 2) indirect conversion. The present paper outlines the operating principles for direct-conversion detectors based on the use of photoconductors. It formulates and reviews the required X-ray photoconductor properties for such applications and examines to what extent potential materials fulfill these requirements. The quantum efficiency, X-ray sensitivity, noise, and detective quantum efficiency factors are discussed with reference to current and potential large area X-ray photoconductors.

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 score0.003
Threshold uncertainty score0.446

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 head0.015
GPT teacher head0.191
Teacher spread0.177 · 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