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Record W2043379997 · doi:10.1002/humu.22053

Standard terminology for phenotypic variations: The Elements of Morphology project, its current progress, and future directions

2012· article· en· W2043379997 on OpenAlexaff
John C. Carey, Judith Allanson, Raoul C. M. Hennekam, Leslie G. Biesecker

Bibliographic record

VenueHuman Mutation · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital limb and hand anomalies
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsTerminologyBiologySubject (documents)Morphology (biology)HumVariation (astronomy)LinguisticsLibrary scienceGeneticsComputer scienceHistory

Abstract

fetched live from OpenAlex

In 2005, the authors of this article formed an international working group to develop standardized definitions and terms to describe the physical variations used in human phenotypic analyses. This project, which came to be known as the Elements of Morphology, resulted in six articles proposing consensus definitions for almost 400 phenotypic variations of the head and face; periorbital region; ear, nose, and philtrum; mouth and lips; and hands and feet. Every variation was accompanied by a representative figure depicting the feature. The articles were published in the January 2009 issue of the American Journal of Medical Genetics Part A and are available for free access on both the Journal's Web page and a National Institutes of Health-based site. The publication of the Elements' definitions has spawned an ongoing dialogue about the proposed terms to describe the phenotype. The working group considered the six articles as only the first step in the process, and four more articles on proposed terminology for the trunk, genital region, skin, and remainder of the limb terms are in preparation. The secondary outcome of the Elements project is the provision of a working methodology for the establishment of standardized terminology and definitions for phenotype analysis in general.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.318
Teacher spread0.294 · 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 designOther design
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

Citations31
Published2012
Admission routes1
Has abstractyes

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