MétaCan
Menu
Back to cohort
Record W2132995317 · doi:10.7567/jjap.52.072202

X-ray Photon Counting Using 100 MHz Ready-Made Silicon P–Intrinsic–N X-ray Diode and Its Application to Energy-Dispersive Computed Tomography

2013· article· en· W2132995317 on OpenAlexfundno aff
Hajime Kodama, Manabu Watanabe, Eiichi Sato, Yasuyuki Oda, Osahiko Hagiwara, Hiroshi Matsukiyo, Akihiro Osawa, Toshiyuki Enomoto, Shinya Kusachi, Shigehiro Sato, Akira Ogawa

Bibliographic record

VenueJapanese Journal of Applied Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsPhysicsOpticsPhoton countingDiodeSilicon drift detectorPhoton energyMaterials sciencePhotonDetectorOptoelectronics

Abstract

fetched live from OpenAlex

X-ray photons are directly detected using a 100 MHz ready-made silicon P–intrinsic–N X-ray diode (Si-PIN-XD). The Si-PIN-XD is shielded using an aluminum case with a 25-µm-thick aluminum window and a BNC connector. The photocurrent from the Si-PIN-XD is amplified by charge sensitive and shaping amplifiers, and the event pulses are sent to a multichannel analyzer (MCA) to measure X-ray spectra. At a tube voltage of 90 kV, we observe K-series characteristic X-rays of tungsten. Photon-counting computed tomography (PC-CT) is accomplished by repeated linear scans and rotations of an object, and projection curves of the object are obtained by linear scanning at a tube current of 2.0 mA. The exposure time for obtaining a tomogram is 10 min with scan steps of 0.5 mm and rotation steps of 1.0°. At a tube voltage of 90 kV, the maximum count rate is 150 kcps. We carry out PC-CT using gadolinium media and confirm the energy-dispersive effect with changes in the lower level voltage of the event pulse using a comparator.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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 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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJapanese Journal of Applied PhysicsSame topicAdvanced X-ray and CT ImagingFrench-language works237,207