Functional Resolution: The Reactive Geometric Labeling Model (RGLM) and the Entropy Anomaly
Hi everyone,
I have been working on a functional approach to the MS 408 text-image relationship, and I am excited to share a formal model that provides a reproducible explanation for the manuscript's low entropy.
Instead of looking for a natural language or a complex cipher, my research focuses on Reactive Geometric Labeling (RGLM / MEGR in Spanish). The core thesis is that the "labels" and text blocks are isomorphic to the visual morphology, density, and spatial distribution of the illustrations.
Key findings of the model:
• Isomorphic Determinism: The word length and prefix/suffix distribution (like the D/C and O/SH families) correlate directly with the geometric complexity of the drawing.
• Entropy Resolution: The low entropy isn't a linguistic feature but a functional one; the "vocabulary" is constrained by the recurring visual patterns it labels.
• Predictability: The model allows us to predict certain lexical clusters based on the specific arrangement of botanical or pharmaceutical elements in the folios.
I have registered the full methodology and the preliminary report on Zenodo to ensure open access and peer review.
Full Paper / DOI: You are not allowed to view links. Register or Login to view.
I would love to hear your thoughts, especially from those focused on computational linguistics and pattern recognition. I am open to testing the model against specific folios suggested by the community.
Best regards, Emmanuel Jiménez Independent Researcher
![[Image: C3TKW2Wj]](https://ibb.co/C3TKW2Wj)
"Figure 1: Application of the Reactive Geometric Labeling Model (RGLM) on folio 2r. Note the correlation between visual complexity and specific lexical clusters."
Hi everyone,
I have been working on a functional approach to the MS 408 text-image relationship, and I am excited to share a formal model that provides a reproducible explanation for the manuscript's low entropy.
Instead of looking for a natural language or a complex cipher, my research focuses on Reactive Geometric Labeling (RGLM / MEGR in Spanish). The core thesis is that the "labels" and text blocks are isomorphic to the visual morphology, density, and spatial distribution of the illustrations.
Key findings of the model:
• Isomorphic Determinism: The word length and prefix/suffix distribution (like the D/C and O/SH families) correlate directly with the geometric complexity of the drawing.
• Entropy Resolution: The low entropy isn't a linguistic feature but a functional one; the "vocabulary" is constrained by the recurring visual patterns it labels.
• Predictability: The model allows us to predict certain lexical clusters based on the specific arrangement of botanical or pharmaceutical elements in the folios.
I have registered the full methodology and the preliminary report on Zenodo to ensure open access and peer review.
Full Paper / DOI: You are not allowed to view links. Register or Login to view.
I would love to hear your thoughts, especially from those focused on computational linguistics and pattern recognition. I am open to testing the model against specific folios suggested by the community.
Best regards, Emmanuel Jiménez Independent Researcher
"Figure 1: Application of the Reactive Geometric Labeling Model (RGLM) on folio 2r. Note the correlation between visual complexity and specific lexical clusters."
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