Softcomputing in Identification of the Origin of Voynich Manuscript by Comparison with Ancient Dialects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it