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Record W4366179118 · doi:10.3390/micro3020031

Nanometals and Metal Ion Pollution from Dental Materials in Dental Environment

2023· article· en· W4366179118 on OpenAlex
Ana Carla B. C. J. Fernandes, Rodrigo França

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicro · 2023
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMercury (programming language)Amalgam (chemistry)DentistryEnvironmental pollutionMaterials scienceEnvironmental scienceMedicineChemistryEnvironmental protectionComputer science

Abstract

fetched live from OpenAlex

The dental environment is being polluted with metals from dental materials in many ways, mainly due to aerosol-generating procedures; this could affect the long-term well-being of dentists, dental students, and dental personnel. The current dental pollution incorporates metallic nanoparticles, which are highly reactive and quickly become airborne, especially those particles that become unbound in the bulk composition. In addition, liquid mercury or mercury vapors may be released from dental amalgam, causing concerns in the dental community. In our study, we reviewed the behavior of metallic elements present in dental materials, their routes of exposure, and their potentially toxic effects on the dental team. This review found that skin and lung disorders are the most harmful effects of metallic exposure for dentists, dental students, and dental personnel. Therefore, chronic exposure to low concentrations of metals in the dental environment, especially in nanosized forms, should be further investigated to improve the environmental matrix, material choice, and safety protocols.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score1.000

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)0.0010.001

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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