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Record W2079303133 · doi:10.1118/1.2799490

The x‐ray light valve: A low‐cost digital radiographic imaging system

2007· letter· en· W2079303133 on OpenAlexaff
I. Koprinarov, Christie Ann Webster, Robert MacDougall, J. A. Rowlands

Bibliographic record

VenueMedical Physics · 2007
Typeletter
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and Bioengineering
KeywordsDigital radiographyRadiographyComputed radiographyDigital imagingImage resolutionComputer scienceImage qualityMedical physicsComputer visionNuclear medicineOpticsArtificial intelligenceDigital imageMedicineRadiologyImage processingPhysicsImage (mathematics)

Abstract

fetched live from OpenAlex

In recent years new digital x-ray radiographic and fluoroscopic systems based on large-area flat-panel technology have revolutionized our capability of producing x-ray images. However, such imagers are extraordinarily expensive and their rapid image acquisition capability is not required for many applications such as radiography. Here we report a novel approach to achieve a high-quality digital radiographic system at a cost which is only a small fraction of competitive digital technologies. The results demonstrate that our proposed x-ray light valve system has excellent spatial resolution and adequate sensitivity compared to existing technologies.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.004
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 head0.236
Teacher spread0.229 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2007
Admission routes1
Has abstractyes

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