Revisiting the Latin vocabulary of <i>Terminologia Histologica</i>: I. Nouns
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".