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Record W4391456295 · doi:10.59657/2993-0863.brs.23.008

Orally Transferred Aphorisms of Dentistry

2023· article· en· W4391456295 on OpenAlexaff
Louis Z.G. Touyz

Bibliographic record

VenueDentistry and Oral Health Care · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsMcGill University
Fundersnot available
KeywordsDentistryMedicineOrthodontics

Abstract

fetched live from OpenAlex

Dentistry has existed for millennia. Wisdom derived from experience has produced principles of practice that influence dentistry. Many of these are oral advisories circulate among dental health care workers in spoken words but are rarely written down. Aim: This article accumulates some of the important orally transferred aphorisms (OTA) and traditions, records them and deconstructs their implications, reasons and significance. Discussion: These oral advising principles have evolved from experience and each act as a dictating guide for policy and activities in the practice of dentistry. Conclusion: Orally transferred aphorisms have been passed down from generation to generation of dentists; those recorded here will be added to, as long as dentistry evolves as a profession to yield further accumulated wisdom of dental health care workers.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.043
Scholarly communication0.0060.005
Open science0.0010.007
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.089
GPT teacher head0.361
Teacher spread0.272 · 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 designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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