Technical Specimen: Barometric Praxinomics in Use A practical descriptor-reliability audit for materials modelling Prepared by: Joseph Maxwell Context: Systemica Engineering / GIL–EDK / Barometric Praxinomics Purpose: Demonstrate how the measurement framework is used in practice on a real tabular materials dataset. 1. Executive summary This document demonstrates a practical use case of Barometric Praxinomics as a measurement layer for systems under pressure. The example used here is a materials machine-learning workflow for shear modulus prediction. Instead of asking only which descriptors rank highly in a model, the method asks whether those descriptors remain reliable when modelling conditions are disturbed. The practical aim is simple: identify which descriptors are stable enough to deserve further technical attention, and which should remain caution-labelled as fragile, target-adjacent, unstable, or artefact- prone. This matters because materials machine-learning models are often used for both prediction and interpretation. A feature may rank highly in one fitted model while behaving inconsistently under changes in data split, perturbation level, model family, or preprocessing condition. The materials-facing paper frames this directly: descriptor reliability can be inspected separately from baseline importance, and stable descriptors should be treated as candidates for follow-up rather than as proof of physical truth. The method therefore provides a practical evidence layer between: raw model output → descriptor reliability audit → cautious follow-up decision It does not claim to replace experiments, domain expertise, DFT, physical validation, or materials judgement. It provides a structured way to decide what is worth investigating next. 2. Problem being addressed A standard machine-learning workflow might produce: model score feature ranking feature importance plot This is useful, but incomplete. In materials settings, descriptors often carry physical meaning. Columns such as density, band gap, formation energy, bulk modulus, elastic anisotropy and Poisson ratio are not neutral labels. They can easily be read as physical explanations. The risk is that a descriptor becomes trusted because it ranked highly once. The practical problem is: predictive importance is not the same as descriptor reliability. The current behavioural feature-auditing paper states the shift clearly: static ranking tells us what the model used at one point, while a behavioural trace asks what happens when modelling conditions change. The audit records drift, variability, recurrence, rank divergence and negative-control behaviour. So the core question becomes: Did the descriptor remain reliable under pressure, or did it only look important under one comfortable model condition? 3. Method overview The method is a perturbation-based descriptor reliability audit. It uses a local CSV, selects a numeric target, trains baseline models, perturbs input descriptors, recomputes feature influence, and records how descriptor behaviour changes. The MVP walkthrough describes the working demo as a minimal behavioural feature- audit run on a local CSV. It uses numeric descriptor columns, fits Ridge and Random Forest baselines, applies Gaussian noise and feature dropout, recomputes importance, calculates descriptor drift D_j, calculates descriptor variability S_j, aggregates repeated-seed results, and exports evidence objects including stability tables, audit decision tables, c