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| 100 sheets of stolen vellum |
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Posted by: JustAnotherTheory - 17-04-2026, 07:43 AM - Forum: Physical material
- Replies (45)
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I had this thought experiment lately. Assuming that the VMS was not written in a scriptorium (i.e., not in an "official" place filled with dozens of professional scribes where one could easily notice that someone is writing a hundreds of pages of secret text in broad daylight), we could assume that it was written in a more secluded place, outside the scope of prying eyes.
One way to do so could be to take the vellum from a scriptorium using theft, and then write the MS in a "safe place". After all, iron gall ink was not difficult to make "at home", so all that was needed for writing the manuscript DYI-style was a lot of vellum.
So I wonder, how easy would it be to steal 100 sheets of vellum from a scriptorium, and whether such a theft would go unnoticed (are 100 sheets "a lot" or "a little" in the scope of a professional workshop)? Would such a theft be recorded in the inventory of a scriptorium?
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| Title: Solving the Voynich Rules: A 100% Consistent Structural Decoding and Mathemati |
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Posted by: Keishi Oi - 16-04-2026, 12:53 AM - Forum: The Slop Bucket
- Replies (1)
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Dear Voynich Manuscript Researchers,
My name is Keishi Oi, an independent researcher based in Japan. I am writing to share the mathematical proof and the decryption methodology of the Voynich Manuscript, which I have named the "OI-2026 Dismantling Protocol."
Core Finding:
The Voynich Manuscript is not written in a natural language, nor is it a simple substitution cipher. Through mathematical modeling, I have proven that the text is a "Combinatorial Data Matrix" — a broadly defined artificial language (comparable to modern Data Description Languages) generated by a deterministic automaton.
Evidence & Data:
The complete mathematical proof, including the calculation of the 11.05 bits word entropy, the extraction of the 17x73 primary register map, and the 100% error-free syntax parsing of the entire combinatorial space, has been made open access.
You can download the full paper (PDF) and the dataset via Zenodo here:
You are not allowed to view links. Register or Login to view.
(Note: The full manuscript has also been submitted to Cryptologia and is currently under peer review.)
I believe the OI-2026 protocol mathematically answers many of the statistical anomalies (such as the extreme Zipf's law deviations) that this community has long debated.
I welcome your rigorous verification, mathematical critiques, and open discussions.
Regarding the methodology and the use of AI:
I am fully aware of the community's concerns regarding LLM-generated "slop." I wish to clarify that I utilized AI strictly as a tool for Python script generation and English translation to facilitate international dissemination of my findings.
Crucially, the core discovery — the 17x73 register map and the four-stage logic architecture — was NOT generated by AI. These structures were identified through deterministic mathematical analysis and verified in independent Python environments (Google Colab/VSCode) to prevent any LLM hallucinations.
Furthermore, please understand that the modern IT terminology I employ (such as "OS," "registers," "Boot," and "Termination") is intended strictly as a functional metaphor. These terms are used to translate the impersonal, abstract behaviors of the manuscript's combinatorial data structure into a format that is comprehensible to human researchers.
If you suspect this is "AI slop," I invite you to stop judging by the style and start judging by the data. The methodology is fully disclosed, and the dataset is public. I welcome any researcher to run the code on the Zenodo repository and attempt to falsify the results through direct replication.
Best regards,
Keishi Oi
Independent Researcher
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| About the construction of lines in the MS |
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Posted by: quimqu - 15-04-2026, 08:25 AM - Forum: Analysis of the text
- Replies (58)
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In this You are not allowed to view links. Register or Login to view., when I was thinking of how to determine if the lines and paragraph have some structure, I got the idea of creating features for the gaps that make the line splits. So see the properties of the words just before the line split and just after.
The idea was to avoid looking at the page as an image and instead work only from the transcription. I turned each paragraph into a sequence of word gaps. For every gap between two consecutive words, I asked whether it was a real line break or just an interior gap.
The model I created was deliberately simple. It did not use exact words as memorized items. It used short word-type signatures and short local segment features. On the left side of a candidate break it looked at the last word and the last two-word tail. On the right side it looked at the first word and the first two-word head. It also measured whether the candidate cut would leave a line length close to the typical line length of that section. So the score had four parts: end quality, start quality, fit to line length, and a smaller central transition term.
So, making it short, a "good line ending" means that the word just before the break, and the small tail that ends the line, look like the endings that real lines usually have in that section. A "good line opening" means that the word just after the break, and the short opening sequence after it, look like real line openings. This is not a claim about magical words, it is a claim about recurrent statistical shapes.
The main validation was done only on Herbal and Biological, because they are large enough and relatively coherent. I used paragraph-level cross-validation, so train and test never shared the same paragraph. I also repeated the whole split with several random seeds. Then I added a null control: within each training paragraph I randomly permuted which gaps were marked as boundaries, trained the same model on those fake labels, and tested on the real held-out paragraphs. If the signal was an artifact of the pipeline, the null should also score well.... It did not.
These are the main validation results.
| Section | Real AUC mean | Real AUC sd | Null AUC mean | Null AUC sd |
|---|
| Biological (balneological) | 0.891 | 0.057 | 0.459 | 0.119 |
| Herbal | 0.905 | 0.036 | 0.461 | 0.131 |
Those values are the most important part of the finding. The real model stays very high across repeated train/test splits. The null model collapses close to chance. That does not prove everything, but it is strong evidence that the detection is not coming from a trivial leak or from learning the same paragraphs it later predicts.
The next question was whether the score was really mixing several weak signals or whether the individual components also worked on their own.... they did.
| Section | End-quality AUC | Start-quality AUC | Fit-quality AUC |
|---|
| Biological (balneological) | 0.708 | 0.763 | 0.797 |
| Herbal | 0.752 | 0.763 | 0.780 |
This means that real line breaks are not only associated with a better local transition. They are also associated with better line endings, better next-line openings, and better line-length fit. In other words, the break seems to happen where the text can be segmented into two pieces that both look well formed.
The direction of the effect is also consistent in both sections.
| Section | Boundary end quality | Interior end quality | Boundary start quality | Interior start quality | Boundary fit quality | Interior fit quality |
|---|
| Biological (balneological) | -2.019 | -2.564 | -1.774 | -2.508 | -1.531 | -2.531 |
| Herbal | -1.788 | -2.349 | -1.798 | -2.444 | -1.453 | -2.663 |
The scores are log-like and therefore negative, so the less negative values are the better ones. Real line breaks systematically look better than interior gaps on all three dimensions.
After that I tested whether the mechanism was section-specific or partly shared. First I did a cleaner transfer between the two large sections. Herbal was used to score Biological, and Biological was used to score Herbal. Then I trained on both of them together and only then applied the model descriptively to the other sections.
| Training | Test section | Boundary AUC | End AUC | Start AUC | Fit AUC |
|---|
| Herbal | Biological (balneological) | 0.804 | 0.667 | 0.690 | 0.753 |
| Biological (balneological) | Herbal | 0.809 | 0.627 | 0.659 | 0.785 |
That is lower than within-section validation, as expected, but still strong. So the mechanism is not just a local quirk of one section.
When Herbal and Biological are pooled and used as a source model, the score still separates real boundaries from interior gaps in the other sections as well.
| Training | Test section | Boundary AUC |
|---|
| Herbal + Biological | Marginal stars only | 0.958 |
| Herbal + Biological | Text-only | 0.859 |
| Herbal + Biological | Pharmaceutical | 0.782 |
| Herbal + Biological | Zodiac | 0.746 |
| Herbal + Biological | Astronomical | 0.739 |
| Herbal + Biological | Cosmological | 0.727 |
I would still call those transfer values descriptive rather than fully validated, because those sections were not cross-validated on themselves in the same way. But they are high enough to suggest that at least part of the lineation logic is shared across the manuscript.
The last part was to look at what actually defines a good ending and a good beginning. The model is not using exact words as rules. It is using recurrent word-type patterns. So the result is better read as families of endings and openings rather than individual tokens.
For Biological, line endings are enriched in short and medium forms such as ol..ly, ol..dy, or..ry, ol..ol, ok..ky, and some larger qo... tails. Line openings are enriched in families like so..dy, so..ey, so..or, so..ol, sa..ar, sa..in, ds..dy, dc..dy, tc..dy, again with some recurring qo... heads.
For Herbal, line endings are enriched in short and medium forms such as ..am, ..dy, da..an, da..am, ok..am, ot..am, ch..ry, and some repeated two-word tails involving da..in. Line openings are enriched in families like yc..or, yc..ol, yc..ey, so..in, so..or, so..ol, dc..ey, dc..dy, ds..dy, yt..dy, tc..in, and ta..ar.
A compact summary of the strongest recurrent families is this:
| Section | Good line endings tend to look like | Good line openings tend to look like |
|---|
| Biological | ol..ly, ol..dy, or..ry, ol..ol, ok..ky, some qo... tails | so..dy, so..ey, so..or, so..ol, sa..ar, sa..in, ds..dy, dc..dy, tc..dy |
| Herbal | ..am, ..dy, da..an, da..am, ok..am, ot..am, ch..ry, some da..in tails | yc..or, yc..ol, yc..ey, so..in, so..or, so..ol, dc..ey, dc..dy, ds..dy, yt..dy, tc..in |
So the main result is not that the manuscript has special boundary words that never appear elsewhere. It is that line breaks tend to occur where the preceding segment ends in a statistically good way and the following segment begins in a statistically good way, with the added constraint that the resulting line length also fits the section.
That is why the detection values are so high. The model is not reading the same paragraphs twice. It is learning which kinds of endings, beginnings and line-length fits are typical in training paragraphs, and then finding the same structure in held-out paragraphs. The null control shows that this does not survive random relabelling. So the effect looks real.
I think the safest formulation is this: in Herbal and Biological, line breaks are statistically structured rather than arbitrary. They can be detected very well out of sample because real boundaries have better line-ending quality, better next-line-opening quality, and better line-length fit than ordinary interior gaps. The same scoring scheme also transfers surprisingly well to several other sections, which suggests that at least part of the lineation mechanism may be shared across the Voynich manuscript.
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| La Sfera by Gregorio Dati |
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Posted by: JustAnotherTheory - 14-04-2026, 07:45 PM - Forum: Imagery
- Replies (5)
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La Sfera by Gregorio Dati is an early 15th century North Italian illustrated cosmographical poem. Dati lived an unusual life and I highly recommend that you read about it here: You are not allowed to view links. Register or Login to view.. Some highlights are his 26 children, 4 wives, twice escaping the bubonic plague, and many voyages.
Anyway, La Sfera was very popular in the 1400s and was copied many times. In fact, it became so popular at some point that it rivalled the famous Bellifortis. But then, when Gregorio Dati died, the interest in La Sfera instantly diminished, and in fact all but disappeared.
The reason I'm posting this here is because (in my opinion) the imagery in La Sfera seems similar to that of the VMS. Let me begin by a direct parallel to the VMS f68 foldouts:
Here are some relevant illustrations in La Sfera.
In fact, there are so many such star maps with the sun above and the moon below, I don't have enough space to show them all here. There is a collaborative online project called "La Sfera Project" that aims to gather information about all the copies of La Sfera (You are not allowed to view links. Register or Login to view.). I also encourage you to take a look.
The VMS also contains a disk with a T-O globe in the middle. This is also a feature of La Sfera:
Again, I'm just scratching the surface, as there are dozens of such diagrams.
Perhaps a more impressive parallel to the VMS are the vast networks of canals and lakes, some of which also seem to be coloured in the same manner as the VMS. It almost looks like the VMS, without the nymphs in the water:
There are, here again, hundreds of such drawings.
Here's another parallel to the VMS:
Many, many other beautiful illustrations (that I picked randomly from a pool of 30 copies of La Sfera) also remind one of the VMS:
And we also have the swallowtail merlons in at least one copy:
______________________________
Links to all manuscripts here: You are not allowed to view links. Register or Login to view.
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| A structural hypothesis: Voynich text as an operational volvelle system |
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Posted by: YannichFR - 14-04-2026, 08:45 AM - Forum: The Slop Bucket
- Replies (1)
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Hi everyone,
I would like to share a working hypothesis based on structural analysis of several folios. This is not a claim of decipherment, but an attempt to model how the system might function.
1. Starting point
Across multiple folios (e.g. f18r, f19r, f20v, f2v, f99v), I observed:- Strong repetition of specific word families (qok-, chol/chor, daiin, -aiin, dy, etc.)
- Stable patterns with small local variations
- Recurring positions (beginning, middle, end of lines)
- Sequences that appear more procedural than descriptive
This led me to shift from a “translation” approach to a structural and operational one.
2. Words as functional particles
Instead of treating words as lexical units, I treat them as functional elements within a system.
Examples of recurring families:- qok- → often appears in initiating or structuring positions
- chol / chor / cho → very frequent, possibly action-related blocks
- daiin / aiin → often appears as a pivot or transition point
- dy / dar / dal / dary → frequently near terminal positions
- endings in -aiin → may indicate a transformed or final state
These elements combine in highly regular ways.
3. From sentences to sequences
Some lines (especially in f86v4 and f20v) do not behave like simple sentences, but rather like multi-step sequences:- repetition of blocks
- reappearance of the same families mid-line
- multiple “pivot-like” elements (e.g. aiin appearing more than once)
This suggests something closer to:
a sequence of operations or states, not a grammatical sentence
4. Multi-object behavior
In some folios (e.g. f99v), the same structures apply to different object markers (e.g. {plant}, {hole}).
This suggests:- the system is not purely botanical
- words do not describe objects directly
- they define roles or transformations applied to objects
5. Hypothesis: volvelle-like system
Based on this, I propose a tentative model:- Recurring word families correspond to functional layers (or “disks”)
- Individual words correspond to positions or states within those layers
- A line encodes a sequence of transitions or alignments
In other words, the text could be a linear encoding of a circular or combinatorial system, similar in spirit to a volvelle.
6. Why this might make sense
Such a system would:- explain strong repetition and modularity
- allow controlled variation (parameters)
- be usable as an operational tool rather than a descriptive text
- match the need for an efficient, reusable system
7. What this does NOT claim- I am not claiming specific translations
- I am not claiming that a physical volvelle is proven
- I am not claiming this explains everything
This is only a structural hypothesis.
8. What I would like feedback on
I would really appreciate feedback on:
- Whether others have observed similar positional constraints
- Whether the “functional particle” approach seems plausible
- Whether the volvelle analogy is useful or misleading
- Any counterexamples where this structure clearly fails
If useful, I can share more detailed breakdowns of specific folios and sequences.
Thanks in advance for your thoughts — I’m trying to test whether this approach can hold up under scrutiny.
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| Armenian grammar + Latin pharmaceutical vocabulary (67% word recognition) |
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Posted by: x.lyren - 13-04-2026, 08:44 PM - Forum: The Slop Bucket
- Replies (4)
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Hi everyone,
I've been running a computational linguistic analysis on the VMS text (58 iterative Python runs over the IVTFF H transcription, 37,025 words) and wanted to share the results for discussion and criticism.
**TL;DR:** The analysis suggests the text may use Armenian grammatical function words combined with Latin pharmaceutical terminology — the kind of mixed-language writing documented in 15th-century Armenian medical texts. 67% of words can be mapped to identifiable Armenian/Latin forms. A blind test shows this is 7.4x above random baseline (6%), so it's not just pattern-matching noise.
## The core findings
### 1. Eight exact matches with Classical Armenian function words
| Decoded | Armenian | Meaning | Frequency |
|---------|----------|---------|-----------|
| vor | vor | who/which | 2x |
| zi | zi | because | 103x |
| or | or | day | 448x |
| gal | gal | to come | 9x |
| tal | tal | to give | ~10x |
| ban | ban | thing/word | ~10x |
| am | am | year / instrumental | 619x |
| ce | ker | food/preparation | ~400x |
The last one (`ce` = Bedrosian Dictionary's `ker` = "food") is particularly interesting because `cocei` (451x) and `coced` (604x) — the two most common prefixed words — decompose as `co` + `ce` + suffix, functioning as the main recipe instruction verb.
### 2. Latin material names (not Armenian)
| Decoded | Latin | Meaning |
|---------|-------|---------|
| ol | oleum | oil (692x) |
| col | cola | strain (574x) |
| cor | cortex | bark (257x) |
| sal | sal | salt (75x) |
| sol | solve | dissolve (196x) |
| can | canna | reed/tube (163x) |
| car | caro | flesh/meat (85x) |
The split is consistent: **grammar = Armenian, ingredients = Latin**. This matches the documented practice of Amirdovlat Amasiatsi and other 15th-century Armenian physicians who used Armenian sentence structure with foreign technical terms.
### 3. The EVA triple consonant system matches Armenian phonology
The EVA ligature system (k/ch/kch, p/ch/pch, t/ch/tch) encodes the Armenian three-way stop distinction (voiceless/aspirated/voiced). This matches the Cilician Middle Armenian consonant shift documented by Vardanyan (1999):
- EVA `kch` → /g/ (Armenian voiced)
- EVA `pch` → /b/ (Armenian voiced)
- EVA `tch` → /j/ (Armenian voiced)
### 4. Two different ligatures for two different suffixes
EVA `aiin` and `ain` — previously treated as identical — show different distributional patterns:
- `ain` → `-an` (genitive/dative, grammatical contexts)
- `aiin` → `-am` (instrumental, appears next to measurement terms)
### 5. Medieval pharmaceutical number system
| Decoded | Meaning | Frequency |
|---------|---------|-----------|
| d | 1 dose (℥j) | 946x |
| i | 1 unit | 621x |
| s | half (semis) | 292x |
| sd | half-dose (℥ss) | 548x |
| si | 1½ | 453x |
| gd | drop (gutta) | 170x |
These appear adjacent to material names in exactly the positions expected for recipe dosages.
### 6. Blind test validation
| Test | Recognition |
|------|-------------|
| Real Voynich | 44.8% |
| Random text (same char frequencies) | 6.0% ± 0.1% |
| Random text (uniform) | 2.0% |
Gap: +38.8 pp (7.4x). The method captures real structure, not noise.
### 7. All sections use the same vocabulary
Every section tested — herbal, biological/"bathing", pharmaceutical, recipes, astronomical, cosmological — uses identical vocabulary (62-80% recognition). The "bathing" pages and "cosmological" pages contain the same recipe language as the pharmaceutical section.
## Sample translation (f75r line 36, 100% recognized)
**EVA:** `sol keedy qokeedy qokey okar otar dar dar dy`
**Decoded:** `sol ced coced cocei ocar odar dar dar d`
**English:** DISSOLVE your-food! PREPARE-your-food! PREPARE! To-here the-medicine. Medicine, medicine, ℥j (one dose).
## What I'm NOT claiming
- This is not a complete decipherment. ~33% of words remain unidentified.
- The exact phonetic values are not all finalized (particularly t→d vs t→t).
- Sentence-level coherent translation is only partial.
- I cannot identify which plants or diseases the recipes describe.
- The word `bor` (wine?) matches neither Latin nor Armenian.
## What I am claiming
- The text contains real linguistic structure (validated by blind test)
- Armenian function words appear at statistically significant rates
- The mixed Armenian grammar + Latin vocabulary pattern matches documented 15th-century Armenian pharmaceutical writing practice
- The text reads as pharmaceutical recipes: materials + preparation + dosage
## Reproducibility
All 58 Python scripts, the full IVTFF data, output files, the Bedrosian dictionary extract, and the Amirdovlat research compilation are available. Happy to share the GitHub repo if there's interest.
I'd particularly welcome:
- Criticism of the methodology (am I overfitting?)
- Input from anyone who reads Classical Armenian
- Comparison with other decipherment attempts
- Statistical critique of the blind test
Thanks for reading. Looking forward to the discussion.
# Appendix: Full decoding rules, vocabulary, and sample translations
## A. Complete Decoding Rules (EVA → phonetic value)
Processing order matters — longer sequences are matched first:
```
LIGATURES (multi-character):
chckh → tsh chcth → tst chck → tsh chct → tst
kch → g pch → b tch → j lch → gh
dch → dj fch → v cth → th
aiin → am iin → in
chee → e che → (silent) cho → kho chy → i
ch → h
qo → co ok → oc ot → ot ol → ol
da → da dy → d ai → a ar → ar
am → am ed → ed ee → e he → (silent)
sh → z in → n pl → pl
SINGLE CHARACTERS:
y → i k → c t → d h → (silent)
All other letters (a, e, i, o, l, d, s, p, r, n, m, c, f, g, u, b, v) → unchanged
```
**Key insight:** The `aiin` vs `ain` distinction is critical. These are two different ligatures encoding two different suffixes (-am instrumental vs -an genitive/dative). Previous transcription analyses treated them as identical.
## B. Complete Identified Vocabulary
### Stems — Armenian origin (8 exact matches)
| Decoded | Armenian | English | Evidence |
|---------|----------|---------|----------|
| vor | vor | who/which | Relative pronoun, exact match |
| zi | zi | because | Conjunction, 103x, exact match |
| or | or | day | 448x, exact match |
| gal | gal | to come | Verb, exact match |
| tal | tal | to give | Verb, exact match |
| ban | ban | thing/word | Noun, exact match |
| am | am | year / with (INSTR) | 619x, exact match |
| ce | ker | food/preparation | Bedrosian Dict. confirms |
### Stems — Armenian near-matches (4)
| Decoded | Armenian | English | Difference |
|---------|----------|---------|------------|
| dar | derman | medicine/remedy | dar ≈ derm (abbreviation?) |
| dam | dram | drachma | dam ≈ dram (missing r) |
| khor | khot | herb/grass | khor ≈ khot (r↔t) |
| sar | serm | seed | sar ≈ serm (abbreviation?) |
### Stems — Latin origin (10)
| Decoded | Latin | English | Freq |
|---------|-------|---------|------|
| ol | oleum | oil | 692x |
| col | cola | strain (verb) | 574x |
| cor | cortex | bark | 257x |
| sal | sal | salt | 75x |
| sol | solve | dissolve (verb) | 196x |
| can | canna | reed/tube | 163x |
| car | caro | flesh/meat | 85x |
| cal | calidus | warm/hot | 49x |
| cear | cera | wax | ~30x |
| ceol | cera+oleum | wax-oil | ~35x |
### Other identified stems
| Decoded | Meaning | Notes |
|---------|---------|-------|
| zol | sap/liquid | 176x, dominant in Herbal section |
| lc | milk | 86x (ragozott), lac? |
| bor | wine | 30x, NOT Latin/Armenian — Hungarian/Turkic? |
| bol | bolite/bolus | Armenian bole (medicinal clay) |
| opi | opium | 9x |
| dol | dose (unit) | 239x |
| ded | they give | 73x |
| com | mix (verb) | 16x |
| ad | add (verb) | 5x |
| tor | grind (verb) | ~20x |
| dal | give (verb) | 322x |
### Suffixes (case system)
| Suffix | EVA ligature | Function | Armenian parallel |
|--------|-------------|----------|-------------------|
| -an | ain | genitive/dative ("to/for") | Grabar GEN/DAT -an |
| -am | aiin | instrumental ("with/by") | Middle Armenian INSTR |
| -ar | ar | allative ("toward") | Word-formative suffix |
| -ed | edy/eedy | uncertain ("also"? "and"?) | Debated |
| -i | y | genitive ("of") | Grabar GEN -i |
| -d | dy | possessive ("your") | Grabar POSS |
| -n | in | definite article | Middle Armenian DEF |
| -al | al | infinitive ("-ly"/"-ing") | Uncertain |
### Prefixes
| Prefix | EVA | Function | Evidence |
|--------|-----|----------|----------|
| co- | qo | imperative ("prepare!") | 6,951x; exclusively before material nouns/verbs |
| oc- | ok | demonstrative ("this/that") | 2,350x; exclusively before case suffixes |
| o- | o | accusative ("the...[object]") | 4,894x; before inflected stems |
**Evidence for distinct prefix functions (Run42 discovery):**
`co-` + ced(604x), can(578x), car(174x), cal(197x) — but oc+ced = 0x, oc+can = 0x
`oc-` + an(365x), ed(235x), ar(148x), ol(86x) — but co+an = 41x, co+ed = 26x
The distribution is almost perfectly complementary.
### Measurements
| Decoded | Medieval equiv. | Meaning | Freq |
|---------|----------------|---------|------|
| d | ℥j | 1 dose | 946x |
| i | j | 1 unit | 621x |
| s | ss | half (semis) | 292x |
| sd | ℥ss | half-dose | 548x |
| si | — | 1½ | 453x |
| gd | gtt | 1 drop (gutta) | 170x |
| dd | ℥ij | 2 doses | 30x |
| ii | ij | 2 | 19x |
| dsd | ℥j½ | 1.5 doses | 66x |
| dsdi | ℥j½+1 | 1.5 doses + 1 | 86x |
## C. Sample Translations (10 pages, best lines)
### You are not allowed to view links. Register or Login to view. — Biological section (74.7% recognized)
**Line 22 (100%):**
EVA: `odar shey qokain chedyor shey kar chedy sar`
→ `the-medicine 1½ PREPARE-in-reed! part 1½ meat ℥j seed`
**Line 36 (100%):**
EVA: `sol keedy qokeedy qokey okar otar dar dar dy`
→ `DISSOLVE! food.POSS PREPARE-yours! PREPARE! to-here the-medicine medicine medicine ℥j`
**Line 38 (83%):**
→ `PREPARE-yours! PREPARE-yours! PREPARE-yours! PREPARE-yours! PREPARE-yours! [?]`
*(5x repetition — compare Amirdovlat's "And do this for six days!")*
**Line 26 (100%):**
→ `to.the oil DISSOLVE! to.the oil PREPARE-[?] ADD!`
### You are not allowed to view links. Register or Login to view. — Biological "bathing" section (79.5% recognized)
**Line 10 (100%):**
→ `PREPARE they-give them-GIVE ℥ss PREPARE-yours! PREPARE also this-also ℥.with`
**Line 26 (100%):**
→ `oil PREPARE-meat! ℥ss STRAIN them-GIVE the-drop`
**Line 28 (100%):**
→ `GIVE! ℥ss PREPARE-yours! ℥ss to.the ℥.with`
### You are not allowed to view links. Register or Login to view. — Biological "bathing" section (76.1% recognized)
**Line 13 (100%):**
→ `PREPARE-yours! oil food.POSS PREPARE-in-reed! this-also food.POSS ℥ ℥j PREPARE they-give ℥j`
**Line 18 (100%):**
→ `oil ℥j PREPARE-in-reed! medicine toward to-this`
### You are not allowed to view links. Register or Login to view. — Pharmaceutical section (76.5% recognized)
**Line 22 (100% of decodable):**
→ `2 with-this STRAIN day oil`
### You are not allowed to view links. Register or Login to view. — Recipe section (72.0% recognized)
**Line 17 (100%):**
→ `seed 1½ PREPARE! food.POSS PREPARE! |dz| PREPARE-warm! the-℥.GEN day with`
**Line 51 (100%):**
→ `ss.with food.GEN 1½ the-ss.with`
### f85r1 — Cosmological "9-rosette" page (67.0% recognized)
**Line 22 (83%):**
→ `[?] medicine ℥j the-r.with likewise this-also`
## D. Blind Test Details
**Method:** Generate 37,025 random "words" using same character frequency distribution as real Voynich. Apply identical decoding rules and dictionary. Measure % recognized. Repeat 100 times.
**Results:**
- Real Voynich: **44.8%** (note: lower than 67% because the blind test used a simpler matching algorithm without prefix/suffix decomposition)
- Random (frequency-matched): **6.0% ± 0.1%** (range: 5.8-6.4%)
- Random (uniform alphabet): **2.0% ± 0.1%**
- Bigram-preserving random: **49.7% ± 0.3%**
The bigram result (49.7%) deserves discussion. It means that if you preserve which characters tend to follow which characters (2-gram statistics), you get similar recognition. This could mean: (a) our decoding captures bigram structure rather than word meaning, OR (b) the Voynich's bigram patterns ARE the linguistic structure we're decoding, which is expected if the decoding is correct. The 6% frequency-matched result confirms the dictionary alone doesn't produce false positives.
## E. What remains unidentified
The ~33% unidentified words fall into these categories:
1. **Short function words** (o, r, l, co — possibly scribal marks or separators)
2. **Armenian-phonology words** (ci, gi, dzi, cci — likely Armenian verb forms or particles requiring native speaker input)
3. **Compound measurements** (dsdi, dsd, lsd — combined dosage notations)
4. **Voiced-consonant stems** (ged, bed, djed, ob, oj, dj — words beginning with Armenian voiced consonants, probably identifiable with a larger Armenian dictionary)
5. **Long compound words** — likely multi-morpheme constructions we haven't decomposed yet
## F. Reproduction
All code is Python 3. The analysis requires:
- `voynich_data.txt` (IVTFF transcription, freely available)
- `armenian_vocab_transliterated.txt` (896 entries from Bedrosian Dictionary)
- 58 analysis scripts (voynich_run01.py through voynich_run59.py)
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| The Genoese Gambit |
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Posted by: R. Sale - 11-04-2026, 08:25 PM - Forum: Theories & Solutions
- Replies (5)
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Everything depends on interpretation. Useful interpretation depends on knowledge derived from relevant information, not from opinion. Relevant information comes from historical facts. In the VMs there are a few places where the artist makes use of historical facts.
One example is the use of historical heraldry on VMs White Aries (f71r). The structural duality based on radial versus non-radial interpretations of the orientation of the two blue-striped tub patterns is a clear indication that this is intentional duplicity.
Does the reader know the armorial insignia of the Roman Catholic pope who instituted the tradition of the cardinal's red galero?
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| theory tries to connect everything in the scripts |
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Posted by: Tanay_alias_CG - 11-04-2026, 04:05 PM - Forum: Theories & Solutions
- Replies (2)
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I’ve been looking into the Voynich Manuscript and had a thought I wanted to run by people here.
Some analyses suggest that certain visual features in the plant drawings (like root structure) might correlate with patterns in the text. That got me thinking about whether the different sections of the manuscript are more connected than they first appear.
What if the plant drawings aren’t meant to represent real plants, but instead combinations of different parts (like roots, stems, leaves) that stand for ingredients or categories?
Then the section with the women in liquid could be related to how those things affect the body, possibly something to do with reproduction or internal processes (not necessarily literal bathing).
And the zodiac diagrams might act as a timing system, since astrology was often linked to medicine back then.
So instead of separate sections, it could be one system where plants, the body, and astrology all interact.
I might be completely off, but I’m curious:
has anyone looked at whether these visual patterns (like root types or other structures) connect to specific kinds of outcomes in the biological or zodiac sections?
Just trying to see if linking these parts together leads anywhere.
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