The measurement of tooth whiteness by image analysis and spectrophotometry: a comparison
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.
Bibliographic record
Abstract
Digital image capturing and analysis techniques have been used to measure the colour of teeth and to compare with spectrophotometric results and visual observations. A non-linear image analysis approach was developed and, for the colour range of human teeth, allows device-dependant digital camera colour data to be quantitatively transformed to Commission Internationale de l'Eclairage (CIE) colorimetric values. With reference to a CIE standard illuminant, two different lighting arrays have been used. For flat and non-translucent white and yellow surfaces, spectrophotometric results showed that this transformation achieves required accuracy. It was found, in all of the present studies, which included measurements on the VITA Lumin Vacuum shade guide and extracted teeth, that spectrophotometry invariably underestimated values of the CIE whiteness index. However, the results from these two types of measurement correlated well. There was also a reasonably good correlation between earlier data obtained by visual assessment and the present data by the two instrumental methods. For extracted teeth, both instrumental methods used in this work did not confirm a whitening effect for 2-min brushing with toothpaste, but did show significant whitening results for bleaching with 15% hydrogen peroxide.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it