Embodied Ecological Sensing via Thermodynamic Models

@techreport{cnl2026embodied, author = {}, title = {Embodied Ecological Sensing via Thermodynamic Models}, institution = {Canemah Nature Laboratory}, year = {2026}, number = {CNL-TN-2026-014}, month = {february}, url = {https://canemah.org/archive/document.php?id=CNL-TN-2026-014}, abstract = {This note proposes and demonstrates a fundamental reconceptualization of ecological monitoring: the transition from systems that represent landscape state to systems that embody it. Drawing on recent advances in thermodynamic computing and the physics of far-from-equilibrium systems, we outline an architecture for integrating energy-based models into the Macroscope ecological observatory. The approach encodes ecosystem patterns not as stored baselines but as topological structures within energy landscapes. Incoming sensor data acts as physical bias on a Boltzmann machine mesh, where deviations from normal state manifest as mathematical tension rather than calculated anomalies. We describe SOMA (Stochastic Observatory for Mesh Awareness), a proof-of-concept implementation at Canemah Nature Laboratory that validates this approach. Three meshes—weather, species, and ecosystem—run continuous inference against live sensor feeds. Initial operation confirms basic viability: 288 inference cycles over 72 hours, with the ecosystem mesh successfully detecting a cross-domain anomaly invisible to single-domain analysis. The framework offers three novel capabilities: temporal topology that embeds multiple timescales in mesh architecture rather than data summaries; absence detection through relational structure that makes missing elements create positive signal; and cross-domain resonance where couplings across environmental and biological domains emerge from learned topology rather than specified algorithms.} }