What this is. Every text from a pre-registered experiment that pushed 128 features of Google DeepMind's Gemma Scope 2 dictionary into Gemma 3 4B IT, at eight strengths, against two kinds of control, with every text scored blind by two independent AI readers. It answers a practical question for anyone steering with sparse-autoencoder features: which features actually change what the model writes, how hard do you have to push, and what predicts it? Made by the Aipotek project: an instrument for exploring language models by turning their internal "dials". Professionally copy/pasted by Scott, with Claude (Anthropic) writing the code, running the analysis and drafting this card. Everything here…