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Record W2025423274 · doi:10.1116/1.1460897

Active pixel sensor architectures in a-SiH for medical imaging

2002· article· en· W2025423274 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2002
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of TorontoUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPixelImage sensorLinearityAmplifierNoise (video)DetectorFixed-pattern noiseImage resolutionSIGNAL (programming language)Reading (process)Computer scienceElectrical engineeringElectronic engineeringOptoelectronicsPhysicsOpticsCMOSArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

The most widely used architecture in large area amorphous silicon (a-Si) flat panel imagers is the passive pixel sensor (PPS), which consists of a detector and a readout switch. While the PPS has the advantage of being compact and amenable towards high-resolution imaging, reading the low PPS output signal requires external circuitry such as column charge amplifiers that produce additional noise and reduce the minimum readable sensor input signal. This work presents a voltage mediated active pixel sensor (APS) on-pixel readout circuit for diagnostic medical imaging to minimize external component count and hence external readout noise sources. Preliminary results indicate excellent APS linearity along with a pixel readout time suitable for mammography or radiography.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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