Cardinality / Range Dynamic Inventory

1. Related files found

Path	Type	Granularity	Main fields	Used?	Why
`README.md`	markdown	project-level	benchmark scope, evaluation layers, model roster, repo layout	yes	Defines project framing and final scientific takeaway.
`Paper\69b27219c555c38a69bb2156\sections\introduction.tex`	tex	paper-section	evaluation narrative, scoring interpretation, main-text phrasing	yes	Aligns the analysis language with the paper draft.
`Paper\69b27219c555c38a69bb2156\sections\evaluation_methodology.tex`	tex	paper-section	evaluation narrative, scoring interpretation, main-text phrasing	yes	Aligns the analysis language with the paper draft.
`Paper\69b27219c555c38a69bb2156\sections\evaluation.tex`	tex	paper-section	evaluation narrative, scoring interpretation, main-text phrasing	yes	Aligns the analysis language with the paper draft.
`Paper\69b27219c555c38a69bb2156\sections\appendix_scoring_standard.tex`	tex	paper-section	evaluation narrative, scoring interpretation, main-text phrasing	yes	Aligns the analysis language with the paper draft.
`doc/synthetic_data_scoring_protocol_v0_4.md`	markdown	metric-level	step-by-step validation protocol, applicability rules	yes	Cross-checks the implementation against the documented scoring standard.
`src/evaluation/synthetic_validation_v4.py`	python	metric-level	exact score logic, per-column detail fields, channel composition	yes	Primary source for the exact discrete and continuous formulas.
`Evaluation\validation\runs\20260426_145322\summaries\validation_details__all_datasets.jsonl`	jsonl	dataset-model asset with column-level detail	dataset_id, model_id, cardinality_range discrete/continuous details, per-column real/synthetic support and ranges	primary	Main analysis source because it preserves the per-column structures needed for dynamic buckets.
`Evaluation\validation\runs\20260426_145322\summaries\validation_summary__all_datasets.csv`	csv	dataset-model asset	dataset_id, model_id, cardinality_range_score and other validation scores	secondary	Used for schema cross-checking and run-level coverage confirmation.
`Evaluation\validation\runs\20260426_145322\manifest.json`	json	run-level	dataset_count, asset_count	secondary	Used to select the highest-coverage validation run.
`logs/runs/ (legacy/v1 benchmark and grounded-SQL artifacts)`	directory	grounded-SQL run artifacts	query_results.jsonl, run manifests, traces	context only	Confirms repository positioning as workload-grounded, but not used for cardinality/range metric values.

2. Primary analysis source

Selected run: 20260426_145322
Details file: Evaluation\validation\runs\20260426_145322\summaries\validation_details__all_datasets.jsonl
Coverage before deduplication: 513 assets across 51 datasets
Coverage after deduplication and alias normalization: 496 dataset-model assets across 51 datasets and 14 normalized models
Column-level analysis units built: 33815
Duplicate normalized dataset-model pairs resolved: 17 dropped assets

3. Field availability

Source	dataset id/name	model/generator	discrete/continuous channel	score	real distinct	synthetic distinct	real min/max	synthetic min/max	column name	overall validation score
validation_details__all_datasets.jsonl	yes	yes	yes	yes	yes (discrete per-column)	yes (discrete per-column)	yes (continuous per-column)	yes (continuous per-column)	yes	yes
validation_summary__all_datasets.csv	yes	yes	no	channel-level only	no	no	no	no	no	yes

4. Field mappings used by the loader

asset.model_id -> normalized model via alias map {rtf: realtabformer}.
report.validation_channels.cardinality_range.details.discrete_profile.per_column[].real_distinct -> real_distinct_count.
report.validation_channels.cardinality_range.details.discrete_profile.per_column[].syn_distinct -> synthetic_distinct_count.
report.validation_channels.cardinality_range.details.continuous_profile.per_column[].real_min/real_max -> real_min / real_max.
report.validation_channels.cardinality_range.details.continuous_profile.per_column[].syn_min/syn_max -> synthetic_min / synthetic_max.
report.validation_channels.cardinality_range.discrete_profile_score / continuous_profile_score remain attached as official channel-level references.

5. Files used only for cross-checking or context

Older validation runs under Evaluation/validation/runs/20260426_100004, 20260426_100103, 20260426_100210, and 20260426_105754 were excluded because they only cover 1--2 datasets.
The run-level summary CSV is not rich enough for dynamic-bucket analysis because it lacks per-column support and range envelopes.
Synthetic CSVs under SynOutput/ and SynOutput-5090/ were not read directly because the unified validation details already materialize the necessary real-vs-synthetic comparisons.

6. Schema inconsistencies and missing fields

Model alias inconsistency: rtf appears alongside realtabformer; the analysis normalizes both to realtabformer.
The full validation run contains duplicate dataset × normalized_model assets for 17 pairs, mostly across SynOutput vs SynOutput-5090; the analysis keeps the newest generation timestamp and records dropped duplicates.
Coverage is incomplete for several generators even in the highest-coverage run, so the effective panel is not a complete 51 × 14 matrix.
Windows-style paths are embedded in the unified evaluation outputs, but they are treated as metadata strings rather than local file dependencies.

7. Coverage gaps that matter for interpretation

The normalized roster still reaches all 14 target generator names, but some generators have much smaller dataset coverage than the benchmark maximum.
codi, forestdiffusion, and tabdiff are especially sparse, so their high-dynamic conclusions should be read as conditional on smaller sample sizes.

8. Final decision for the main analysis

Use validation_details__all_datasets.jsonl as the main source.
Use column-level units whenever possible.
Keep discrete and continuous channels separate until the final cross-channel summaries.
Treat the discrete unit score as a derived support-retention view built directly from official per-column support-loss counts; note this explicitly in the report.