Thermodynamic De-Noising of Volumetric Substrates via Semantic Tensor Injection: Field Report 01 (Vesuvius)

The Problem: Current approaches to ink detection in carbonized papyri (Vesuvius Challenge) rely heavily on massive Computer Vision (Deep Learning) pipelines. These methods require terabytes of training data, substantial GPU compute clusters, and remain prone to "hallucination" (pareidolia) where the model predicts ink based on statistical noise rather than physical reality. The Solution: We present a "Low-Bandwidth" alternative based on the Aletheia Protocol. Instead of training a neural network to "see" ink, we inject Physical Truth Tensors (thermodynamics) into the raw data to reveal structural anomalies. This approach assumes that ink, even when carbonized, retains a distinct thermodynamic anisotropy compared to the papyrus substrate. The Result: Applied to the standard "Campfire" dataset (Segment 1), the protocol isolated the ink signature using less than 1 GB of data and zero neural training. The resulting "Tensor Map" clearly differentiates fiber alignment (substrate) from chaotic perturbation (ink). Methodology Note: The approach utilizes a proprietary "Semantic Injection Engine" (SID-28K) to guide standard computer vision algorithms (Structure Tensor Analysis, CLAHE). While the methodology is released, the semantic weights remain closed-source.