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Effectiveness of wet and dry mercury vapour suppressant systems in a faculty of dentistry clinic

2004· article· en· W2056296759 on OpenAlexaff
E.J. Sutow, G.C. Hall, Cathy MacLean

Bibliographic record

VenueJournal of Oral Rehabilitation · 2004
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMercury (programming language)ScrapChemistryDentistryMetallurgyMaterials scienceMedicine

Abstract

fetched live from OpenAlex

The objective of this study was to determine the effectiveness of a liquid and a dry commercial mercury vapour suppressant system. Measurements were made in a student dental clinic, using a mercury vapour detector for periods up to 76 weeks. The two products examined were Mercon vap liquid in a stock jar and the Mercon tainer dry jar system. Amalgam scrap jars were removed from the study when the mercury vapour concentration in the jars exceeded the arbitrary cut-off criterion of 0.05 mg Hg m(-3). Results showed that the mercury vapour concentration in the liquid system exceeded the cut-off criterion in 44 weeks or less, whereas the dry system remained below the detection limit (0.01 mg Hg m(-3)) for the maximum measurement period of 76 weeks. It was concluded that the dry system is more effective and reliable than the liquid system. The reliability of the liquid system may be influenced by contact of amalgam scrap with the portion of the inner wall of the jar that is not covered by liquid. It is proposed that amalgam scrap contaminates the wall with mercury during its insertion.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.390
Teacher spread0.357 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2004
Admission routes1
Has abstractyes

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