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Record W2108894887 · doi:10.1177/0022034512443689

Rediscovering Sig Socransky, the Genius and His Legacy

2012· article· en· W2108894887 on OpenAlexaboutno aff
Ricardo Teles, Flavia Teles, Walter J. Loesche, Max A. Listgarten, Daniel H. Fine, Jan Lindhe, Kenneth A. Malament, A. D. Haffajee

Bibliographic record

VenueJournal of Dental Research · 2012
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsnot available
FundersKarolinska InstitutetInternational Association for Dental Research
KeywordsPeriodontologyCertificatePersonalityWork (physics)MedicineMedical educationPsychologyDentistryFamily medicineLibrary sciencePsychoanalysisEngineeringComputer science

Abstract

fetched live from OpenAlex

Some individuals make contributions so vital to their field of knowledge that their names become almost synonymous with that field. This is the case of Sig Socransky and the field of periodontal microbiology. Sig Socransky, or simply Sig, was born in Toronto, Canada and received his DDS degree from the University of Toronto in 1957. He studied microbiology and periodontology at Harvard, receiving a certificate in 1961. That same year he was recruited to work as a Research Associate at the Forsyth Dental Center. In 1968, he was nominated Senior Member of the Staff and Head of the Department of Periodontology. During his 50-year career at Forsyth, Sig published over 300 manuscripts, keeping an average of 7 publications per year. His work had an indelible impact in the fields of periodontology and oral microbiology. All these accomplishments pale in comparison with the impact that Sig had on a personal level. We have collected testimonials from some of his former students, closest collaborators, and friends in an attempt to give readers an insight into Sig's personality. We hope we can offer those who knew him through his work a glimpse of how it felt to interact with this remarkable individual.

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)0.003
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 score0.287
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.084
GPT teacher head0.411
Teacher spread0.327 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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