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An interview with

2017· article· en· W2716858008 on OpenAlexaboutno aff
Paul Gange

Bibliographic record

VenueDental Press Journal of Orthodontics · 2017
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNatural language processingComputer science

Abstract

fetched live from OpenAlex

A. in marketingfrom the John Carroll University.Paul has had a career in the orthodontic industry that spans over three decades.He has been directly involved with the development of orthodontic adhesives, sealants and cements for over thirty-four years.Some of the highlights of his successful career include: the development of the first "No Mix" adhesive, the advancement of newest bonding adhesives and techniques, as well as the development of many of the products that you use in your everyday bonding procedures both direct and indirect.Paul Gange has also been a guest speaker in numerous study clubs, universities, continuing education courses and Regional Component meetings in the United States, Canada, Europe and the Far East.Also, he has been guest lecturer with several leading clinicians worldwide.His publications include numerous journal articles and textbook chapters, and he holds several patents.Paul is married to Sharon, the Vice President at Reliance Orthodontic Products.Together, they started the business in 1982.They have two children, Paul Jr., who is the National Sales and marketing manager at Reliance and a daughter, Nicole, who is a first-year resident in the orthodontic program at Case Western Reserve University.Paul enjoys golf, horse racing, exercising at the gym, spending time with my family and being a part of solving orthodontic bonding problems within the greatest industry in the world.There is nobody more qualified to answers questions on adhesives.Thank you Paul for making our lives so much easier!I also would like to thank our select group of interviewers.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.054
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.353
Teacher spread0.286 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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