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Record W4386982264 · doi:10.1016/j.identj.2023.07.646

Mercury vapor from pre-capsulated dental amalgam according to storage temperature

2023· article· en· W4386982264 on OpenAlexaboutno aff
Miss Ji-Eun Kim, Jae‐Sung Kwon, Kwang‐Mahn Kim

Bibliographic record

VenueInternational Dental Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)Christian ministryElemental mercuryChemistryMaterials scienceOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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