docs: clarify MPHF indexing, storage layout, and distance traits

Formalize the two-phase MPHF indexing architecture and update Phase 6 to use `evidence.bin` for direct kmer extraction. Simplify the evidence and unitig storage layouts to flat packed formats enabling O(1) random access. Introduce aggregation traits (`ColumnWeights`, `CountPartials`, `BitPartials`) to support additive distance metric decomposition across partitions. Narrow the documented scope from metagenomic to individual genome datasets, and replace speculative open questions with concrete implementation specifications.

docmd/implementation/persistent_bit_vec.md

@@ -236,3 +236,35 @@ impl LayerData for PersistentBitMatrix {
     fn read(&self, slot: usize) -> Box<[bool]>   { self.row(slot) }
 }
 ```
+
+---
+
+## Aggregation traits — `obicompactvec::traits`
+
+`PersistentBitMatrix` implements two aggregation traits used by `LayeredStore<S>` for cross-layer and cross-partition distance computations.
+
+### ColumnWeights
+
+```rust
+impl ColumnWeights for PersistentBitMatrix {
+    fn col_weights(&self) -> Array1<u64>   // = self.count_ones()
+}
+```
+
+`col_weights()[c]` = number of set bits in column `c` across all slots.
+
+### BitPartials
+
+```rust
+impl BitPartials for PersistentBitMatrix {
+    // Self-contained partials (additive across layers)
+    fn partial_jaccard(&self) -> (Array2<u64>, Array2<u64>)   // (inter, union)
+    fn partial_hamming(&self) -> Array2<u64>                   // differing bits
+
+    // Provided finalisations
+    fn jaccard_dist_matrix(&self) -> Array2<f64>
+    fn hamming_dist_matrix(&self) -> Array2<u64>
+}
+```
+
+`partial_jaccard` returns `(inter, union)` as a pair because `union` is not reconstructible from per-column `count_ones()` — it depends on both columns simultaneously. Both components are additively decomposable across `(partition, layer)` pairs; the final `jaccard_dist_matrix()` is computed from their element-wise sums.