where w 1 = 0.97 and w 2 = -0.43 are determined by fitting to expert validation. We categorize route scores into three groups: "Good" for scores between 0 and 5, "Plausible" for scores between 5 and 9, and "Bad" for scores between 9 and 15. Figure presents the confusion matrix comparing route scores with expert ratings, demonstrating a strong correlation with a Pearson correlation coefficient value of 0.92. This suggests that our scoring is aligned well with the expert assessments. Table presents the precision, recall, and F1-score metrics for each rating class. The model achieves a robust overall accuracy of 70%, with particularly strong performance in distinguishing between "good" and "bad" routes. This is evidenced by the high F1 scores of 0.80 and 0.77 for these classes respectively, indicating the model's reliable ability to identify both high-quality and problematic synthetic routes. Also, the model showed other strengths in assessing route quality across categories. The high recall (0.87) for "Good" routes and perfect precision (1.00) for "Bad" routes indicate that the model excels at identifying promising synthetic routes while being highly selective in flagging problematic ones. The model has good precision (0.74) for "Good" routes, meaning when it predicts a route as good, it's usually correct. For "Bad" routes, the moderate recall (0.62) suggests some challenging routes might be missed, but when the model identifies a route as bad, it's mostly correct. However, the middle "Plausible" class remains an area where the model can be improved, with both precision and recall around 0.5, indicating significant uncertainty in identifying intermediate-quality routes. This could reflect the inherent difficulty in defining and assessing borderline cases. In summary, the model's strengths lie in its reliable identification of good routes while maintaining high confidence in flagging truly problematic ones. For more details of the route analysis please see the Electronic Supporting Information.