Bibliographic record
Abstract
Introduction The fluoridation of municipal water in cities of Canada and the United States for the purpose of reducing dental decay is perhaps the most important and successful public health initiative ever undertaken in these countries. Since its inception more than 50 years ago, water fluoridated at 1 ppm has dramatically reduced the incidence of caries, and this positive effect has reached across all socioeconomic groups. However, since fluoride is a mineral-seeking ion, it is incorporated into bone as well as teeth. The response of bone is known to depend on the dose, and studies in both animal models and in humans have assessed the effect of moderate to high doses of fluoride. As a consequence, it is known that moderate doses of fluoride increase bone mass, making fluoride a potential therapy for osteoporosis (see below). However, chronic exposure to high doses of fluoride (>8 mg/day), while rare in North America, can cause skeletal fluorosis, characterized initially by hypermineralization of bone and later by calcification of ligaments, bone deformation, and other crippling symptoms (Kaminsky et al., 1990). Ingestion of low doses of fluoride through water involves somewhat different mechanisms. Typically, individuals receive less than 5 mg/day of fluoride (less than a tenth of the clinical dose), but it accumulates passively in bone mineral over a timespan of decades.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".