Fluoride's effect on human dentin ultrasound velocity (elastic modulus) and tubule size
Bibliographic record
Abstract
Despite fluoride (F) use in caries prevention, not much is known about its effects on tooth quality. This study evaluated the effect of tooth F concentration ([F]) on selected dentin structural and mechanical properties. Third molars (n = 136) from Toronto, which has 1 part per million (p.p.m.) water [F], Montreal (0.2 p.p.m. water [F]), and Fortaleza (Brazil) (0.7 p.p.m. water [F]), were analyzed for [F], dental fluorosis (DF) severity, ultrasound velocity, and dentin tubule size and density. The enamel [F] was found to vary between 32 and 940 p.p.m., the dentin [F] was found to vary between 110 and 860 p.p.m., while the DF severity varied between TF0 and TF4. The enamel [F] showed no correlation with dentin [F], DF severity, ultrasound velocity, dentin tubule size or density. The dentin [F] correlated with DF severity, dentin tubule size, and ultrasound velocity. DF severity showed a correlation with dentin [F] and ultrasound velocity. It was concluded that dentin [F] is an indicator of dentin structural properties (dentin tubule size and ultrasound velocity), while DF severity is an indicator of dentin mechanical properties (ultrasound velocity).
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".