GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
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This anomaly is indicative of the larger challenge of culturing various microbial species, referred to as microbial “unculturability.” This cannot be explained by the use of agar alone or by the substitution of an alternative gelling agent, but rather by the difficulties in consistently recreating on an agar plate the multi-variable environment in which microbes grow naturally. Given such challenges, the risk of shortages, and the vulnerabilities of the agar supply chain, why is it so difficult to find suitable alternatives?
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