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_compact_int_vec.md

@@ -258,3 +258,51 @@ impl LayerData for PersistentCompactIntMatrix {
     fn read(&self, slot: usize) -> Box<[u32]>    { self.row(slot) }
 }
 ```
+
+---
+
+## Aggregation traits — `obicompactvec::traits`
+
+`PersistentCompactIntMatrix` implements two aggregation traits used by `LayeredStore<S>` for cross-layer and cross-partition distance computations.
+
+### ColumnWeights
+
+```rust
+impl ColumnWeights for PersistentCompactIntMatrix {
+    fn col_weights(&self) -> Array1<u64>   // = self.sum()
+}
+```
+
+`col_weights()[c]` = sum of all values in column `c` across all slots.
+
+### CountPartials
+
+```rust
+impl CountPartials for PersistentCompactIntMatrix {
+    // Self-contained partials (additive across layers, no external parameter)
+    fn partial_bray(&self)                                      -> Array2<u64>
+    fn partial_euclidean(&self)                                 -> Array2<f64>
+    fn partial_threshold_jaccard(&self, threshold: u32)         -> (Array2<u64>, Array2<u64>)
+
+    // Normalised partials (require global col_weights across all layers/partitions)
+    fn partial_relfreq_bray(&self, global: &Array1<u64>)        -> Array2<f64>
+    fn partial_relfreq_euclidean(&self, global: &Array1<u64>)   -> Array2<f64>
+    fn partial_hellinger(&self, global: &Array1<u64>)           -> Array2<f64>
+
+    // Provided finalisations (default implementations on the trait)
+    fn bray_dist_matrix(&self)                                  -> Array2<f64>
+    fn euclidean_dist_matrix(&self)                             -> Array2<f64>
+    fn threshold_jaccard_dist_matrix(&self, threshold: u32)     -> Array2<f64>
+    fn relfreq_bray_dist_matrix(&self)                          -> Array2<f64>
+    fn relfreq_euclidean_dist_matrix(&self)                     -> Array2<f64>
+    fn hellinger_dist_matrix(&self)                             -> Array2<f64>
+}
+```
+
+**Self-contained partials** are additively decomposable: summing `partial_bray()` across all `(partition, layer)` pairs and finalising gives the same result as computing on the combined data.
+
+**Normalised partials** require the global column weights (sum across all layers and all partitions). The `global` parameter must reflect the complete index, not a per-layer sum. The provided `relfreq_bray_dist_matrix()` etc. call `col_weights()` first (pass 1) then the normalised partial (pass 2); when called on a `LayeredStore<LayeredStore<…>>` these two-pass calls cascade automatically through the blanket impls.
+
+**`partial_bray` returns `Array2<u64>`** (sum_min only, not a tuple). The denominator is always reconstructible as `col_weights()[i] + col_weights()[j]`.
+
+**`partial_threshold_jaccard` returns `(inter, union)`** as a pair because `union[i,j]` is not reconstructible from per-column statistics — it depends on both columns simultaneously.