Recent analyses of cosmological hydrodynamic simulations from CAMELS have shown that machine learning models can predict the parameter describing the total matter content of the universe, Ωm, from the features of a single galaxy. We investigate the statistical properties of two of these simulation suites, IllustrisTNG and ASTRID, confirming that Ωm induces a strong displacement on the distribution of galaxy features. We also observe that most other parameters have little to no effect on the distribution, except for the stellar-feedback parameter ASN1, which introduces some near-degeneracies that can be broken with specific features. These two properties explain the predictability of Ωm. We use optimal transport to further measure the effect of parameters on the distribution of galaxy properties, which is found to be consistent with physical expectations. However, we observe discrepancies between the two simulation suites, both in the effect of Ωm on the galaxy properties and in the distributions themselves at identical parameter values. Thus, although Ωm’s signature can be easily detected within a given simulation suite using just a single galaxy, applying this result to real observational data may prove significantly more challenging.