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Record W2626417326

Tay-Sachs and its Prevalence in the Ashkenazi Jewish, French-Canadian, and Louisiana Cajun Population

2017· article· en· W2626417326 on OpenAlexaboutno aff
Kayla Peveler

Bibliographic record

VenueTopSCHOLAR (Western Kentucky University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicJewish Identity and Society
Canadian institutionsnot available
Fundersnot available
KeywordsJudaismGenealogyPopulationDemographyGeographyEthnologyHistoryArchaeologySociology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of my study is to examine the molecular evolution of the HEXA gene and its implications to Tay-Sachs disease. Also, this study intends to compare and contrast the mutations that occur within the various populations at high-risk of developing this disease. My central concept is population genetics in which populations are compared among each other to identify patterns or possible causes of a particular phenomenon. Numerous research studies have been conducted to study specific popul ations for Tay-Sachs, such as the Ashkenazi Jewish population and the Indian communit y. In my study, I intend to compare and contrast the mutations occurring within these p opulations to possibly identify genetic relationships among the populations.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.357
Threshold uncertainty score0.717

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.266
Teacher spread0.240 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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