V (1.5), E (0.2), and especially A (0.0), B (0.0) contributed minimally or not at all in Gradient Boost splits. A zero importance often indicates those variables were never used in the boosted decision trees (or used so little that they did not improve predictive performance). Thus, Gradient Boost findings align generally with Random Forest: Initial Concentration and Surface Area stand out as top features, with some moderate roles for Log Kow, pHpzc, pKa, and Molecular Weight. Temperature (11.3), Log Kow (11.1), pKa (10.9), S (8.3), and Molecular Weight (5.1) cluster at moderate levels. V (1.3) again has low influence, and A (0.0), B (0.0), and E (0.0) do not contribute measurably to improving model performance.