CPU bottleneck in taxonomy aggregation for non-species predictions

When requesting higher taxonomic ranks (genus, family, order, etc.), `TreeOfLifeClassifier.predict()` seems to aggregate the species-level probabilities up the taxonomy tree. Currently, this aggregation uses CPU loops with dictionary lookups - creating a major bottleneck.

With 10k plant images and the current 350K plantae species in the TOL embeddings, that's 3.5 billion dictionary operations on a single CPU-core. I suggest moving this to the GPU with precomputed species-rank mappings up front and using PyTorch `scatter_add` for accumulation. In my 10k dataset, this went from ~130 minutes for family-level predictions to ~4.8 minutes.

Happy to submit PR if interested.

Start at TreeOfLifeClassifier.predict() and trace the taxonomy aggregation path for genus, family, and order predictions. Benchmark the current 10k-image case, then verify that the completed aggregation avoids the CPU dictionary-loop bottleneck and preserves the expected higher-rank probabilities.

Written by the indexing model from the issue text.