Mercury vapor from pre-capsulated dental amalgam according to storage temperature
Bibliographic record
Abstract
The aim of this study was to measure the amount of mercury vapor from one pre-capsulated dental amalgam according to the storage temperature and to investigate whether the storage temperature suggested by the manufacturer or ISO is appropriate for the storage. GK Amalgam and Ultracaps+ were used in this study. One pre-capsulated dental amalgam was placed in a Tedlar bag and vacuumed, and the Tedlar bag was filled with 2 L of (4±2)°C air. The Tedler bag was then stored at one of the three different temperature conditions; (4±2)°C, (23±2)°C or (30±2)°C for 24 hours to obtain air containing mercury vapor. By applying Ontario hydro method, elemental mercury vapor in the Tedlar bag was oxidized in KMnO4-H2SO4 solvent and pre-treated, followed by analyses using the CVAAS method. Total of 5 measurements were obtained from each group. There was a significant difference depending on the storage temperature (P<0.05), and it was confirmed that the amount of mercury vapor increased as the temperature increased. It was evident that storage at (23±2)°C, in compliance with the guidelines of manufacturers and ISO, result in exposure of mercury vapor exceeding 340 to 1,170 % of the exposure amount compare to the standard set by the Ministry of Labor in Korea. It is thought that the amount of mercury vapor can be reduced by lower storage temperature by methods such as refrigerating the pre-capasulated dental amalgam.
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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 | 0.001 |
| 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".