Close orderings of model configurations vary across different seeds and geographic weighting schemes in the Sen1Floods11 evaluation. ^[1] Performance evaluation for surface-water segmentation commonly uses an aggregate metric such as global intersection-over-union (IoU) to rank model configurations. ^[2] The authors conclude that aggregate metrics remain useful for ranking complete configurations, but ranking stability, component attribution, input reliance, and deployment scope require distinct evidence. ^[3] The cross-modal student achieved the highest three-seed mean Intersection-over-Union (IoU) on the Sen1Floods11 dataset. ^[4] On the challenge's five-fold split, the mean Dice was 0.554 and mean lesion-level F1 was 0.528 without scribbles, rising to 0.751 and 0.733 after five correction rounds. ^[5] The autoPET/CT V challenge addresses the complexity of automated lesion segmentation in whole-body PET/CT caused by varying physiological tracer uptake patterns and differing lesion appearances across tracers. ^[6] The submitted model is a scribble-conditioned residual encoder U-Net operating on four input channels: CT, PET, and a sparse scribble map for each of foreground and background. ^[7] The researchers concluded that interaction largely compensates for how well or badly a given model segments unaided. ^[8]