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Record W2058531517 · doi:10.1088/1748-0221/9/11/c11001

CZT detector technology for medical imaging

2014· article· en· W2058531517 on OpenAlexaff
K. Iniewski

Bibliographic record

VenueJournal of Instrumentation · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsRedlen Technologies (Canada)
FundersAir Force Office of Scientific Research
KeywordsDetectorSemiconductor detectorElectronicsCadmium telluride photovoltaicsOptoelectronicsX-ray detectorMaterials scienceMedical physicsReading (process)PhysicsNanotechnologyEngineering physicsOpticsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Over the last two decades, the II–VI semiconductors CdTe and CdZnTe (CZT) has emerged as the material of choice for room temperature detection of hard X-rays and soft γ-rays. The techniques of growing the crystals, the design of the detectors, and the electronics used for reading out the detectors have been considerably improved over the last few years. CdTe/CZT materials find now applications in astrophysics, medical imaging and security applications. The paper discusses recent progress in CZT detector technology and outlines possible new application opportunities.

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)0.002
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: none
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0230.013

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 head0.005
GPT teacher head0.245
Teacher spread0.240 · 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

Citations126
Published2014
Admission routes1
Has abstractyes

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