Labelled responses from remote datasets can't reach `HumanLabeledDataset`

Nobody has claimed this yet.

There's no path from the remote dataset loaders to the labelled-data side of scoring.

**What's there now.** `HumanLabeledDataset` is only constructible via `from_csv()`, so scorer evaluation data has to be authored by hand. That's reflected in `datasets/scorer_evals/` — `refusal.csv` is ~105 rows and several of the objective sets are single digits.

**What's being dropped.** `datasets/seed_datasets/remote/` has 61 loaders, and several of the underlying HuggingFace datasets ship human labels alongside model responses. Those are discarded at load time because `SeedDataset` models prompts only. `beaver_tails_dataset.py` states it directly:

> This loader extracts only the prompts (not the responses) and filters to unsafe entries by default.

`aegis_ai_content_safety_dataset.py` is the same shape. `wildguardmix_dataset.py` at least retains its classifier labels in `metadata`, but still produces a `SeedDataset`.

So labelled `(response, score)` pairs are already flowing through the loaders and being dropped, while the one class that needs them can only be fed by hand.

**Proposal.** A second output path off the existing remote loaders producing `HumanLabeledDataset` — reusing the fetch logic and the `HARM_CATEGORY_ALIAS_OVERRIDES` mapping that's already there, but retaining `assistant_response` and the human label.

Suggest starting narrow: Aegis 2.0 only (CC-BY-4.0, so no licence friction for a repo shipping under MIT) and one harm category that maps cleanly, with tests and a generated `scorer_evals` CSV so it's usable on merge.

Happy to build it if the shape is right — is `HumanLabeledDataset` the correct target here, or is there a reason the loaders are prompts-only that I'm missing?

Start with datasets/seed_datasets/remote/aegis_ai_content_safety_dataset.py and the HumanLabeledDataset path, then compare the existing HARM_CATEGORY_ALIAS_OVERRIDES mapping and loader behavior with datasets/scorer_evals/refusal.csv. Add focused tests for the narrow Aegis 2.0 case and generate a usable scorer_evals CSV retaining assistant_response and the selected human label.

Written by the indexing model from the issue text.