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Record W4413795518 · doi:10.11647/obp.0482.02

Helpful Information for Reading the Latin Text

2025· book-chapter· en· W4413795518 on OpenAlexaff
Catherine Tracy

Bibliographic record

VenueOpen Book Publishers · 2025
Typebook-chapter
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsBishop's University
Fundersnot available
KeywordsReading (process)Latin AmericansComputer scienceInformation retrievalLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

This chapter explains some characteristics of Plautine Latin, and the elements of the two most common metres in the play. The first section (called “Plautine Latin”) is a compilation of the main differences between the Latin of Plautus (“Early Latin”) and the Classical Latin that is taught in most introductory Latin textbooks. This includes spelling differences, and word and word-ending differences. The second section (called “The Rhythm of Plautus”) explains the three categories of Plautine metre, what scanning a line of Latin poetry means, and gives specific instructions on the scansion of trochaic septenarii and of iambic senarii.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.674
Threshold uncertainty score0.466

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.6740.481

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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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