feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

Extrae al registry funciones del proyecto interno footprint_aurgi:
- core (6): slugify_ascii, normalize_for_join, cp_provincia_es, infer_provincia_from_cp, safe_read_csv_fallback, csv_to_parquet_duckdb
- geo puras (7): haversine_km, point_in_ring, point_in_polygon, point_in_polygons_bbox, polygon_bbox, extent_with_padding, distance_bucket
- geo I/O (4): load_geojson_polygons, load_boundary_gdf, add_basemap_osm, add_basemap_with_timeout
- valhalla client (4): valhalla_route, valhalla_isochrone, valhalla_isochrones_async, valhalla_matrix_1_to_n
- datascience stats (7): trimmed_mean, geometric_mean, detect_distribution_type, best_central_tendency, summary_stats, kde_density_levels, alpha_shape_concave_hull
- datascience fuzzy (3): fuzzy_merge_adaptive (rapidfuzz), words_to_dataset, remove_words_from_column
- datascience viz (2): plot_kde_2d, plot_heatmap_log
- infra (4): compress_pdf_ghostscript, render_table_page_pdfpages, add_header_logo, osm2pgsql_ingest
- pipelines (4): setup_geo_stack_docker, compute_centers_reachability, generate_isochrones_by_zone, count_points_per_zone
- types geo (4): LonLat, BBox, IsochroneRequest, Centro

Incluye:
- apps/footprint_geo_stack/ (PostGIS + Martin + Valhalla via docker-compose)
- 131/132 tests pasan (1 skip esperado: osm2pgsql en PATH)
- Issue tracker dev/issues/0052-footprint-aurgi-extraction.md
- Atribucion uniforme: source_repo internal:footprint_aurgi, source_license internal-aurgi
- Build con 9 agentes en paralelo (8 wave 1 + 1 wave 2 pipelines)

Tambien commitea trabajo previo no commiteado: aggregate_extraction_results, chunk_with_overlap, clean_pdf_text, merge_entity_aliases, extract_graph_gliner2, extract_relations_mrebel, extract_triples_spacy_es, gliner2/mrebel/marianmt/rebel/spacy_es load_model, parse_rebel_output, translate_es_to_en, issue 0050/0051.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

python/functions/datascience/mrebel_base_load_model.md

@@ -0,0 +1,56 @@
+---
+name: mrebel_base_load_model
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def mrebel_base_load_model(model_name: str = 'Babelscape/mrebel-base', src_lang: str = 'es_XX', tgt_lang: str = 'tp_XX') -> tuple[Any, Any]"
+description: "Variante rapida de mrebel_load_model con checkpoint base (250M params, ~900 MB). Delega completamente en mrebel_load_model. Misma licencia CC BY-NC-SA 4.0 — solo uso no comercial."
+tags: [mrebel, relation-extraction, nlp, model, huggingface, multilingual, seq2seq, datascience, python]
+uses_functions: [mrebel_load_model_py_datascience]
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: []
+params:
+  - name: model_name
+    desc: "ID del modelo en HuggingFace Hub (defecto: Babelscape/mrebel-base, 250M params)"
+  - name: src_lang
+    desc: "codigo de idioma fuente para el tokenizer mBART: 'es_XX' (ES), 'en_XX' (EN), etc."
+  - name: tgt_lang
+    desc: "token de idioma destino del decoder — siempre 'tp_XX'"
+output: "tupla (tokenizer, model) listos para inferencia, cacheados por (model_name, src_lang) en la cache compartida de mrebel_load_model."
+tested: false
+tests: []
+test_file_path: ""
+file_path: "python/functions/datascience/mrebel_base_load_model.py"
+notes: |
+  LICENCIA: Babelscape/mrebel-base esta bajo CC BY-NC-SA 4.0 (Creative Commons
+  Non-Commercial Share-Alike). Solo uso no comercial. NO usar en productos comerciales.
+  
+  Esta funcion es un thin wrapper — NO duplica logica de carga/cache. Toda la
+  logica vive en mrebel_load_model. Util para benchmarks donde se quiere comparar
+  base vs large con la misma interfaz.
+  
+  La cache es compartida con mrebel_load_model (mismo dict _MODEL_CACHE del modulo).
+---
+
+## Ejemplo
+
+```python
+from python.functions.datascience.mrebel_base_load_model import mrebel_base_load_model
+
+# 250M params vs 600M — misma interfaz
+tokenizer, model = mrebel_base_load_model(src_lang="es_XX")
+```
+
+## Comparacion base vs large
+
+| Variant | Params | Size | Latencia CPU/frase | Recall tipico |
+|---------|--------|------|-------------------|---------------|
+| mrebel-large | 600M | ~2.4 GB | 15-30 s | alto |
+| mrebel-base  | 250M | ~900 MB | 5-10 s  | medio |
+
+Para benchmarks de velocidad en graph_explorer, usar base. Para produccion final, evaluar large.