Using solely the cycling protocols and no battery aging data (e.g., only "Class 0" features), we show good but artificially inflated "early-prediction" model performance, compared to the dummy regressor. We stress that this is not an early prediction model, but a model that predicts cycle life as a function of cycling conditions. Table reports the mean absolute percentage error (MAPE) for both the linear model and the random forest model. These results show that by solely using cycling conditions and no aging data, lifetime can be predicted with 26.4% error, a significant improvement over the 41.2% error of the "dummy model". Importantly, this confirms that cycling conditions are predictive, even without aging data, as should be expected .