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Record W4391088601 · doi:10.1002/ca.24137

Revisiting the Latin vocabulary of <i>Terminologia Histologica</i>: I. Nouns

2024· article· en· W4391088601 on OpenAlexaff
Paul E. Neumann, Stephen C. Russell, Lewis Stiles, Nicolás Ernesto Ottone, Mariano del Sol

Bibliographic record

VenueClinical Anatomy · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsUniversity of SaskatchewanMcMaster UniversityUniversity of OttawaDalhousie University
Fundersnot available
KeywordsLinguisticsNounMorphemeNomenclatureVocabularyProper nounMedicineHistoryPhilosophyTaxonomy (biology)Biology

Abstract

fetched live from OpenAlex

Almost 20% of the Latin nouns (193/993) in Terminologia Histologica (TH), the international standard nomenclature for human histology and cytology, display linguistic problems, particularly in the areas of orthography, gender, and declension. Some anatomists have opposed efforts to restore the quality of the Latin nomenclature as pedantry, preferring to create or modify Latin words so that they resemble words in English and other modern languages. A Latin microanatomical nomenclature is vulnerable to the criticism of anachronism, so the requirement for the use of authentic Latin, including derivation of new words from Greek and Latin words rather than from modern languages, if possible, may be even greater than it is for the anatomical nomenclature. The most common problem identified here appears to have been caused by derivation of Latin nouns by addition of -us and -um second declension endings to English words. Many Latin nouns (128) in TH contain one of six morphemes that have been treated this way even though the original Greek words are either first declension masculine or third declension neuter nouns. Ironically, deriving Latin nouns directly from Greek morphemes often results in words that look more familiar to speakers of Romance and Germanic languages than those derived indirectly through modern languages (e.g., astrocyte, collagene, dendrita, lipochroma, osteoclasta and telomere instead of astrocytus, collagenum, dendritum, lipochromum, osteoclastus, and telomerus).

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)0.002
metaresearch head score (Gemma)0.002
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 score0.814
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.095
GPT teacher head0.401
Teacher spread0.306 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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