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Record W4385199969 · doi:10.1002/jdd.13318

American Dental Education Association Proceedings of the 2023 ADEA House of Delegates

2023· article· en· W4385199969 on OpenAlexfundno aff

Bibliographic record

VenueJournal of Dental Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsnot available
FundersUniversity of California, San FranciscoUniversity of North Carolina at Chapel HillDentsply SironaBausch HealthUniversity of TorontoHealth Science Center, University of TennesseeWestern University of Health SciencesRutgers, The State University of New JerseyGlaxoSmithKlineUniversity of Missouri-Kansas CitySchool of Dental Medicine, University of PittsburghUniversity of PittsburghStony Brook UniversityUniversity of MinnesotaAmerican College of DentistsCollege of Dental Medicine, Columbia UniversitySchool of Dentistry, University of MarylandUniversity of ConnecticutAmerican Association of Endodontists FoundationUniversity of MissouriHarvard UniversityColgate-Palmolive Company
KeywordsDental educationDental researchAssociation (psychology)MedicineDentistryFamily medicineMedical educationGerontologyPsychology

Abstract

fetched live from OpenAlex

ADEA relies significantly on outside support for a number of its activities, and numerous organizations provided much-needed assistance since last year's ADEA Annual Session & Exhibition.The ADEA Board of Directors expresses its sincere appreciation to the following companies, organizations, institutions and individuals for their generous support.Those who have supported ADEA activities and events over the past year-from last year's ADEA Annual Session & Exhibition until the start of this year's Annual Session & Exhibition-are listed alphabetically.Most of the companies listed are also Corporate Members of ADEA, and we are especially grateful to them.ADEA is especially grateful to all our sponsors that supported us as we got back to in person meetings and events.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.466
Teacher spread0.434 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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