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
Abstract: Evaluation of the right colour is an important step in restorative dentistry. In history, clinicians started to take the colour with subjective methods. For instance, shade guides were used to compare the teeth with their colour tabs and choose the right one. However, this method presents some issues related to the clinician: everyone perceives colours in different ways, not only because humans differ from each other, but also because they can be affected by local, physiological, physical and psychologically uncontrolled factors, such as fatigue, aging, emotions and lighting conditions. All these factors together contribute to make the subjective method unpredictable. For this reason, new instruments need to be exploited by clinicians in order to describe teeth colour in a more accurate and objective manner, thus applying the objective method. The digital camera, the colorimeter and the spectrophotometer are some of the instruments that can be used to reach this purpose. In both the subjective and objective methods, during determination of the colour, the clinician often focuses on teeth and forgets what surrounds it, like the black background of the mouth or the environmental light. These elements may influence the perception of the colour and, mainly in clinicians with a low level of experience, they could lead to a wrong evaluation of right shades. In order to solve these issues, different strategies can be applied by clinicians, such as making their own shade guide, mixing the objective and subjective methods, or use new devices.
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 | 0.000 | 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