[caiomescouto]

Implements a new metadata capability following the port/adapter architecture:

• StudyMetadataConfig (core/models/metadata_dto.py): config DTO with section gates; from_metadata_layout() stub reserves the future peh_model MetadataLayout wire.
• MetadataInterface (core/interfaces/metadata.py): generic tree-building orchestration (link keys, variable catalogue, datasets, provenance) over the session cache; abstract profile_column / map_data_type primitives implemented by the adapter.
• DataFrameMetadataAdapter (adapters/metadata/): new "metadata" adapter label; non-identifying column profile (counts only) and wire-format dtype mapping.
• Session.create_study_metadata(...): public facade method.
• Population resolved from Study.study_entity_id_list via StudyEntity (StudyPopulation degrades to StudyEntity in the peh model).
• LOD/LOQ control variables excluded by default; derivation lineage (was_derived_from) walked to include enriched/aggregated observations.
• Tests: interface unit, adapter unit, Session-level, and an integration test comparing against the notebooks/example golden tree (peh_demo workflow).
• Docs: session-reference.md (new method + StudyMetadataConfig), adapters.md (metadata area).

[Copilot]

🟡 Changes recommended

Calculation dependencies lose source identity, provenance can break serialization, and missing designs bypass control-variable exclusion.

3 open issues

• Reject or normalize unsupported provenance values · New
• Exclude cached control properties when observation designs are missing · New
• Preserve source observation in calculation input metadata · New

What changed in this PR

Adds study-metadata generation to Session through the existing port/adapter architecture.

Changes:

• Adds configurable metadata trees covering study links, variables, datasets, and provenance.
• Registers a metadata adapter for data-type mapping.
• Adds documentation and unit/integration coverage.

File	Description
tests/​integration/​metadata/​test_study_metadata.py	Checks the demo metadata tree.
tests/​integration/​metadata/​input/​peh_demo.yaml	Supplies demo metadata resources.
tests/​integration/​metadata/​__init__.py	Initializes integration test package.
tests/​core/​session/​test_session_metadata.py	Tests Session delegation and configuration.
tests/​core/​interfaces/​metadata/​test_metadata.py	Tests metadata assembly and serialization.
tests/​core/​interfaces/​metadata/​__init__.py	Initializes interface test package.
tests/​adapters/​metadata/​test_dataframe_adapter.py	Tests data-type mapping.
tests/​adapters/​metadata/​__init__.py	Initializes adapter test package.
src/​pypeh/​core/​session/​session.py	Exposes and registers metadata service.
src/​pypeh/​core/​models/​metadata_dto.py	Defines metadata configuration.
src/​pypeh/​core/​interfaces/​metadata.py	Builds metadata from cached resources.
src/​pypeh/​adapters/​metadata/​dataframe_adapter.py	Maps schema data types.
src/​pypeh/​adapters/​metadata/​__init__.py	Initializes metadata adapter package.
Makefile	Includes metadata integration tests.
docs/​session-reference.md	Documents metadata API and configuration.
docs/​adapters.md	Lists metadata adapter support.

_{Review effort: Balanced}

[Copilot]

🔵 Needs a closer look

Observation-specific calculations can be misattributed, and accepted numeric subclasses can break YAML serialization.

No open issues 🎉

2 previously missed issues

In code that hasn't changed since last review

Normalize numeric subclasses in extra provenance

src/​pypeh/​core/​interfaces/​metadata.py:150

An IntEnum in extra_provenance passes _check_json_value as an integer, but this fallback returns it unchanged. That violates the documented builtin-only output contract and prevents yaml.safe_dump from serializing the accepted value. Normalize integer and float subclasses to builtin types, while preserving booleans.

Preserve calculation metadata per observation and property

src/​pypeh/​core/​interfaces/​metadata.py:534

When two reachable observations specify the same observable property with different calculation designs, the global seen set drops the second calculation. _datasets_section then reuses the first calculation for that property's columns in both datasets through structure_by_op, producing incorrect derivation metadata. Preserve calculation metadata per (observation_id, property_id) and use the dataset's observation context when selecting it. Add a regression test with a shared output property and different calculation designs.

3 resolved since last review

• Preserve source observation in calculation input metadata
• Exclude cached control properties when observation designs are missing
• Reject or normalize unsupported provenance values

_{Review effort: Balanced}