The use of teeth as the site for the in vivo or ex vivo quantification of skeletal strontium by energy-dispersive X-ray fluorescence spectrometry: A feasibility study
Bibliographic record
Abstract
The use of an energy-dispersive X-ray fluorescence spectrometry (EDXRF) system equipped with an 125I source was validated for in vivo and ex vivo quantification of strontium in human teeth. The mean concentration of enamel strontium for an area with a high influx of immigration (Toronto, Ontario, Canada) is also reported. It was found that the mass attenuation of the strontium and calcium X-rays allows for a probing depth of 1.8 mm (Sr Kα) allowing the front central incisors to act as the site for the in vivo or ex vivo quantification of strontium as well as for molars to be used ex vivo for the quantification of enamel strontium. The calcium signal for all teeth studied (n = 42) was found to have a relative standard deviation of 5.7% which allowed for successful normalization of the strontium signal to that of calcium. Validation of the EDXRF method was performed on human molars (enamel thickness of (1.9 ± 0.7) mm, p < 0.05), by graphite furnace atomic absorption spectrometry (GFAAS). The EDXRF method was found to produce equivalent strontium determinations to that of GFAAS (p < 0.05). Our results also demonstrate that for a sample from a large metropolitan area, the fresh enamel strontium concentrations range from 42–301 ppm with a mean strontium concentration of (169 ± 80) ppm (p < 0.05).
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".