The weight of wastes generated by removal of dental amalgam restorations and the concentration of mercury in dental wastewater.
Bibliographic record
Abstract
OBJECTIVE: To determine the amount of amalgam entering the waste stream during removal of dental amalgam restorations. METHODS: Dental amalgam restorations were removed from anatomic replica teeth and natural teeth by means of a tungsten carbide bur, a high-speed handpiece and a conventional suction system. The weight of amalgam particles trapped in the primary and secondary solids separators was determined. Amalgam particles were filtered from wastewater with 15-microm filter paper and weighed. The concentration of total mercury in the effluent collected (by instantaneous flow-through) during the removal of amalgams, with and without an ISO-certified separator, was measured by means of cold-vapour atomic absorption spectrophotometry. RESULTS: About 60% by weight of the amalgam removed was found in the effluent, about a third was retained in the primary solids separator and less than 10% was retained in the secondary solids separator. The ISO-compliant separator reduced the concentration of mercury in the instantaneous flow-through discharge by 99.4%, from 31.2973 mg/L to 0.1800 mg/L. CONCLUSIONS: About 60% of the waste generated during the removal of amalgams escaped the primary and secondary solids collectors and was released into the wastewater. An ISO-certified amalgam particle separator was effective in removing the amalgam from the wastewater.
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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 | 0.000 | 0.000 |
| 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".