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Record W2046478656 · doi:10.1259/dmfr/55276404

Density conversion factor determined using a cone-beam computed tomography unit NewTom QR-DVT 9000

2006· article· en· W2046478656 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

VenueDentomaxillofacial Radiology · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsHounsfield scaleCone beam computed tomographyNuclear medicineMedicineMathematicsTomographyComputed tomographyRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to determine a conversion coefficient for Hounsfield Units (HU) to material density (g cm(-3)) obtained from cone-beam computed tomography (CBCT-NewTom QR-DVT 9000) data. METHODS: Six cylindrical models of materials with different densities were made and scanned using the NewTom QR-DVT 9000 Volume Scanner. The raw data were converted into DICOM format and analysed using Merge eFilm and AMIRA to determine the HU of different areas of the models. RESULTS: There was no significant difference (P = 0.846) between the HU given by each piece of software. A linear regression was performed using the density, rho (g cm(-3)), as the dependent variable in terms of the HU (H). The regression equation obtained was rho = 0.002H-0.381 with an R2 value of 0.986. The standard error of the estimation is 27.104 HU in the case of the Hounsfield Units and 0.064 g cm(-3) in the case of density. CONCLUSION: CBCT provides an effective option for determination of material density expressed as Hounsfield Units.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.233
Teacher spread0.219 · 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