The measurement of tooth whiteness by image analysis and spectrophotometry: a comparison
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.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".