Density conversion factor determined using a cone-beam computed tomography unit NewTom QR-DVT 9000
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".