Bibliographic record
Abstract
Determining the shade of dental materials is a challenging requirement for the restorative dentist. Improper shade selection is the second cause for laboratory remakes, resulting in inefficiency and additional cost, and unnecessary stress for the clinician and patient. The process of shade selection is somewhat subjective, with no consensus on the protocol. This research investigation aimed to develop a novel software application to provide an accurate, objective, and systematic approach to shade determination for teeth, soft tissues, and dental materials. An IOS software application was developed, termed Smile Shade, to facilitate a simple approach for dental shade determination. Smile Shade functions on a high-dynamic-range microcolor sensor with automatic temperature control and inter-device repeatability of <1.0 ∆E. The determination of shade is completed through the evaluation of color based on CMYK, RGB, and LAB, which are different techniques of storing colors. Further research is underway to compare this novel application to the traditional shade tab approach commonly practiced at most dental schools.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".