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Record W4293207001 · doi:10.1002/ca.23946

Substantive changes in the Latin anatomical nomenclature: Sometimes less is more

2022· review· en· W4293207001 on OpenAlexaff
Paul E. Neumann

Bibliographic record

VenueClinical Anatomy · 2022
Typereview
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNounParticipleLinguisticsAdjectiveTerminologyAttributiveNeologismVocabularyNomenclatureMedicineTaxonomy (biology)VerbPhilosophy

Abstract

fetched live from OpenAlex

Substantivation, the use of an adjective or participle as a noun, is commonly used informally to shorten Latin anatomical terms. Dozens of substantives also appear in the international standard anatomical terminology. Most of these are venerable and familiar as nouns in Latin anatomical terms. Examples of Latin nouns derived directly or indirectly from Greek and Latin adjectives and participles are presented here. Although neologisms are said to enrich languages, careful consideration is required before adding to a technical vocabulary. Terms consisting of a substantive or displaying a substantive as the head noun may be vague to learners and nonspecialists.

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)0.004
metaresearch head score (Gemma)0.007
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: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.008
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0090.005

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 head0.280
GPT teacher head0.480
Teacher spread0.200 · 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
GenreReview

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

Citations5
Published2022
Admission routes1
Has abstractyes

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