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Record W2551680132 · doi:10.1002/ca.22805

Elimination of the apposition in Latin anatomical terms

2016· article· en· W2551680132 on OpenAlexaff
Paul E. Neumann

Bibliographic record

VenueClinical Anatomy · 2016
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNominative caseNounMedicineLinguisticsProper nounHomonym (biology)SyntaxNatural language processingAnatomyComputer sciencePhilosophyBiologyVerb

Abstract

fetched live from OpenAlex

The anatomical nomenclature rules require that terms be as short and simple as possible. One common exception to that rule is Latin terms that contain two nouns in nominative case, for example, Musculus masseter and Os ischium. Although these may appear to speakers of other languages to be compound nouns, they are appositions, grammatical structures in which one noun renames, defines or describes the entity named by the other noun. More than 125 terms in Terminologia Anatomica can be simplified, without loss of clarity, by prohibiting use of more than one noun in nominative case in Latin anatomical terms (e.g., Masseter and Os ischii). Clin. Anat. 30:156-158, 2017. © 2016 Wiley Periodicals, Inc.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.006
Scholarly communication0.0040.006
Open science0.0020.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0200.022

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 head0.057
GPT teacher head0.385
Teacher spread0.328 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2016
Admission routes1
Has abstractyes

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