Softcomputing in Identification of the Origin of Voynich Manuscript by Comparison with Ancient Dialects
Bibliographic record
Abstract
The Voynich manuscript is a more than 600-years-old historical manuscript. It is considered one of the most mysterious books in the world. Over the last 100 years, this book has resisted attempts to decipher its content; hence, it is written in an unidentified language. Since the discovery of the manuscript, many known and unknown cryptographers have unsuccessfully tried to deci- pher this book. Also, many mathematical methods have been implemented to determine whether it is a fraudulent historical text or an authentic text containing valuable information. This article aims to show the use of deep learning networks and classical methods to measure the similarity between the individual characters of the alphabet and between other alphabets and Voynich. The first part of the article demonstrates the effectiveness of our method in determining the similarities between individual characters of the Voynich alphabet. In the second part, we find the similarity between the Voynich Manuscript and other individual alphabet sets (languages). In other words, this article shows another possible direction in the research of Voyn- ich manuscript to identify the language dialect family from which Voynich manuscript can theoretically come. The code aims to show how we technically produced the experiment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".