Estimated quantity of mercury in amalgam waste water residue released by dentists into the sewerage system in Ontario, Canada.
Bibliographic record
Abstract
AIM: To estimate the quantity of dental amalgam that Ontario dentists release into waste water. METHODS: Information from a self-administered postal survey of Ontario dentists was combined with the results of other experiments on the weight of amalgam restorations and the quantity of amalgam waste that bypasses solids separators in dental offices. Algorithms were developed to compute the quantity of amalgam waste leaving dental offices when dentists used or did not use ISO 11143 amalgam particle separators. RESULTS: A total of 878 (44.0%) of 1,994 sampled dentists responded to the survey. It was estimated that Ontario dentists removed 1,880.32 kg of amalgam (940.16 kg of mercury) during 2002, of which 1,128.19 kg of amalgam (564.10 kg of mercury) would have been released into waste water in Ontario if no dentists had been using a separator. Approximately 22% of the dentists reported using amalgam particle separators. On the basis of current use of amalgam separators, it was estimated that 861.78 kg of amalgam (430.89 kg of mercury or 170.72 mg per dentist daily) was released in 2002. The use of amalgam separators by all dentists could reduce the quantity of amalgam (and mercury) entering waste water to an estimated 12.41 kg (6.21 kg of mercury, or 2.46 mg per dentist per day). CONCLUSION: Amalgam particles separators can dramatically reduce amalgam and mercury loading in waste water released from dental offices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".