Elevated dentine-lead levels in deciduous teeth collected from remote First Nation communities located in the western James Bay region of northern Ontario, Canada
Bibliographic record
Abstract
Teeth were collected from First Nation schoolchildren inhabiting the remote western James Bay region of northern Ontario, Canada. Lead levels in dentine chips were determined by graphite furnace atomic absorption spectrometry, for naturally exfoliated deciduous teeth. Within exfoliated teeth (one tooth supplied per person), no significant differences in lead concentrations between tooth type were found (P = 0.36). The mean lead concentration of exfoliated teeth of 9.2 microg g(-1) dry weight (N = 61) from this remote region was comparable to levels reported by others for children inhabiting urban centers or residing near smelters. Further, 24.6% (N = 15) had elevated dentine-lead levels ( > 10 microg g(-1)). Lead levels in soil, water, and air have been reported as being low and unimportant sources of exposure for people of the western James Bay area. Evidence is reviewed suggesting that lead contaminated game meat was one source of environmental lead exposure. Consumption data indicate that wildlife is still an important food source for First Nation people of the western James Bay region; 98% (46/47) of the children surveyed consumed some type of wild meat.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".