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Sculpting in Dentistry – Requirements, Methods and Limitations in Aesthetic Dentistry: A Minireview

2021· article· en· W3216159994 on OpenAlexaff
Nidarsh D. Hegde, Vandana Sadananda, Mithra N. Hegde, R. Mathew, Priya

Bibliographic record

VenueJournal of Pharmaceutical Research International · 2021
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversité du Québec à Trois-RivièresInnovation and Economic Development Trois Rivières
Fundersnot available
KeywordsFace (sociological concept)Dental treatmentsComputer scienceDentistryMedicineSociology

Abstract

fetched live from OpenAlex

The skill of sculpting is an interesting and important part of restorative and esthetic dentistry. This review article will help achieve an understanding on what we know about this art, the hurdles we have faced, advances achieved, the limitations we still face and would help to gain knowledge and information on how to further advance in the art of manipulation and sculpting of dental restorations. This article aimed to review available literature on manipulation techniques, to summarize the importance of sculpting and to identify methods to face the current limitations of the art.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.486
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.505
GPT teacher head0.607
Teacher spread0.102 · 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
Published2021
Admission routes1
Has abstractyes

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