{ "license": "CC-BY-NC-4.0; derived from the MerRec dataset, Copyright Mercari, Inc.; see LICENSE-MERREC.md in this directory", "name": "b_bridge_purification", "description": "World bridges recomputed without the catalog entries a decision model rejects as standalone brands; the published betweenness ranking, the post-filtered ranking and the purified ranking over the same window, with a denylist control and a second window.", "captures": [ "b_bridge_purification_all_live.json", "b_bridge_purification_all_canonical_live.json", "b_bridge_purification_sep_oct_live.json" ], "server_binary_sha256": "e2aaf1424e9dd8605835ddb3e09abef2cb5b3629796e3c4a11bb43dac3c9d697", "questions": { "entity_kind": { "type": "choice", "instructions": "In a peer-to-peer marketplace (Mercari), sellers select a `brand_name` from a dropdown. Some entries are real standalone brands, while others are generic catch-all tags, product categories, multi-brand department stores/retailers, or mass-retailer private labels. Classify this `brand_name` entry.", "criteria": { "standalone_brand": "A genuine standalone consumer brand, fashion label, cosmetics brand, electronics/toy manufacturer, or entertainment IP (e.g., H&M, ZARA, Anthropologie, Nike, Free People, Urban Outfitters, Morphe, Tarte, Disney)", "multi_brand_retailer": "A department store, big-box superstore, specialty retail chain, or mall store that sells many third-party or licensed franchises under one roof (e.g., Walmart, Macy's, JCPenney, Target, Sephora, Claire's, Hot Topic, BoxLunch, Spencer's, Kohl's, Nordstrom, Costco)", "superstore_house_brand": "A private-label / house brand owned by a mass big-box superstore (Walmart, Target, Costco, Amazon) spanning home/apparel/general merchandise (e.g., Mainstays, Better Homes & Gardens, THRESHOLD, Time and Tru, No Boundaries, Wild Fable, Universal Thread, Cat & Jack, Room Essentials, Kirkland Signature)", "generic_catchall_or_category": "Not a brand at all, but a generic seller descriptor, vintage tag, infomercial label, or product type mistakenly listed as a brand (e.g., Customized & Personalized, US Vintage, American Vintage, As Seen on TV, Eyeshadow, Handmade, Boutique)" } }, "is_standalone_brand": { "type": "noul", "instructions": "Is this entry a genuine standalone consumer brand rather than a generic descriptor, category name, department store, or big-box private label?", "criteria": { "true": "Distinct standalone brand or creator label with its own brand identity", "false": "Generic catch-all tag, product category, department store/superstore, or big-box house label" } }, "wormhole_pollution": { "type": "score", "instructions": "How severely does this entry act as an artificial cross-community 'wormhole' that conflates unrelated shopping communities in a brand co-liking graph?", "criteria": [ "0 - Cohesive standalone brand representing a real, specific brand affinity", "1 - Broad lifestyle/fashion retailer or specialty store with moderately diverse categories", "2 - Mass-market superstore house label or department store spanning unrelated household and apparel verticals", "3 - Severe graph pollutant: generic non-brand catch-all tag (e.g. 'Customized & Personalized', 'As Seen on TV', 'US Vintage', 'Eyeshadow') or universal big-box superstore ('Walmart')" ] } }, "policy": { "action_question": "is_standalone_brand", "accept_min": 0.7, "review_min": 0.3, "require": { "entity_kind": [ "standalone_brand" ] } }, "control_denylist": { "multi_brand_retailer": [ "Bed Bath & Beyond", "Cabela's", "Cato", "Charming Charlie", "Claire's", "Hobby Lobby", "Hot Topic", "JCPenney", "Kohl's", "Loot Crate", "Macy's", "Nordstrom", "Pier 1 Imports", "Pier One", "Rue21", "Spencer's", "Ulta", "Walmart", "World Market" ], "superstore_house_brand": [ "Better Homes & Gardens", "Cherokee", "Circo", "Faded Glory", "Hyper Tough", "INC International Concepts", "Mainstays", "No Boundaries", "Room Essentials", "SO", "Secret Treasures", "Sonoma", "Style & Co", "Wonder Nation", "Xhilaration", "a.n.a" ], "generic_catchall_or_category": [ "American Vintage", "ART", "Army", "As Seen on TV", "Authentic Original Vintage Style", "Customized & Personalized", "Eyeshadow", "Independent", "Indie", "Lot", "Original Vintage Style", "Rare", "The Unbranded Brand", "US Vintage" ] }, "sql": { "probe": "WITH fans AS (\n SELECT -(e.user_id + 1) AS src, CAST(i.brand_id AS BIGINT) AS dst\n FROM merrec_events e JOIN merrec_items i ON i.item_id = e.item_id\n WHERE e.event_type = 'item_like' AND i.brand_id <> 0\n AND e.event_time >= TIMESTAMP '2023-05-01' AND e.event_time < TIMESTAMP '2023-11-01'\n GROUP BY e.user_id, i.brand_id\n), top_brands AS (\n SELECT dst AS brand_id, COUNT(*) AS fans FROM fans GROUP BY dst ORDER BY fans DESC, dst LIMIT 5000\n), similar AS (\n SELECT first, second, similarity\n FROM cugraph_jaccard(\n edges => (SELECT src, dst FROM fans),\n vertex_pairs => (SELECT a.brand_id AS first, b.brand_id AS second\n FROM top_brands a JOIN top_brands b ON a.brand_id < b.brand_id),\n directed => false)\n WHERE similarity >= 0.02\n), worlds AS (\n SELECT vertex AS brand_id, community AS world\n FROM cugraph_leiden(\n edges => (SELECT first AS src, second AS dst, CAST(similarity AS DOUBLE) AS weight FROM similar),\n directed => false, resolution => 3, seed => 42)\n), bridges AS (\n SELECT vertex AS brand_id, value AS betweenness\n FROM cugraph_betweenness_centrality(\n edges => (SELECT first AS src, second AS dst FROM similar),\n directed => false, normalized => true)\n), neighbors AS (\n SELECT first AS brand_id, second AS neighbor FROM similar\n UNION ALL SELECT second, first FROM similar\n), reach AS (\n SELECT n.brand_id, COUNT(*) AS neighbors, COUNT(DISTINCT w.world) AS worlds_reached\n FROM neighbors n JOIN worlds w ON w.brand_id = n.neighbor\n GROUP BY n.brand_id\n), brands AS (\n SELECT CAST(brand_id AS BIGINT) AS brand_id, MIN(brand_name) AS brand_name\n FROM merrec_items WHERE brand_id <> 0 GROUP BY brand_id\n)\nSELECT br.brand_id, b.brand_name, t.fans, w.world, r.neighbors, r.worlds_reached, br.betweenness\nFROM bridges br\nJOIN worlds w ON w.brand_id = br.brand_id\nJOIN reach r ON r.brand_id = br.brand_id\nJOIN top_brands t ON t.brand_id = br.brand_id\nJOIN brands b ON b.brand_id = br.brand_id\nORDER BY br.betweenness DESC, b.brand_name\nLIMIT 120\n", "context": "WITH target_items AS (\n SELECT CAST(brand_id AS BIGINT) AS brand_id, c0_name, c1_name, category_path, name, price\n FROM merrec_items\n WHERE brand_id IN ({brand_ids})\n), brand_stats AS (\n SELECT brand_id,\n COUNT(*) AS total_listings,\n COUNT(DISTINCT c0_name) AS distinct_c0,\n COUNT(DISTINCT c1_name) AS distinct_c1,\n ROUND(AVG(price), 2) AS avg_price\n FROM target_items\n GROUP BY brand_id\n), top_cats AS (\n SELECT brand_id, category_path, COUNT(*) AS listings,\n ROW_NUMBER() OVER (PARTITION BY brand_id ORDER BY COUNT(*) DESC, category_path) AS rk\n FROM target_items\n WHERE category_path IS NOT NULL\n GROUP BY brand_id, category_path\n), sample_titles AS (\n SELECT brand_id, name,\n ROW_NUMBER() OVER (PARTITION BY brand_id ORDER BY name) AS trk\n FROM target_items\n WHERE name IS NOT NULL\n)\nSELECT bs.brand_id, bs.total_listings, bs.distinct_c0, bs.distinct_c1, bs.avg_price,\n MAX(CASE WHEN tc.rk = 1 THEN tc.category_path END) AS cat1,\n MAX(CASE WHEN tc.rk = 1 THEN tc.listings END) AS cat1_listings,\n MAX(CASE WHEN tc.rk = 2 THEN tc.category_path END) AS cat2,\n MAX(CASE WHEN tc.rk = 2 THEN tc.listings END) AS cat2_listings,\n MAX(CASE WHEN tc.rk = 3 THEN tc.category_path END) AS cat3,\n MAX(CASE WHEN tc.rk = 3 THEN tc.listings END) AS cat3_listings,\n MAX(CASE WHEN st.trk = 10 THEN st.name END) AS title1,\n MAX(CASE WHEN st.trk = 50 THEN st.name END) AS title2,\n MAX(CASE WHEN st.trk = 100 THEN st.name END) AS title3,\n MAX(CASE WHEN st.trk = 200 THEN st.name END) AS title4\nFROM brand_stats bs\nLEFT JOIN top_cats tc ON tc.brand_id = bs.brand_id AND tc.rk <= 3\nLEFT JOIN sample_titles st ON st.brand_id = bs.brand_id AND st.trk IN (10, 50, 100, 200)\nGROUP BY bs.brand_id, bs.total_listings, bs.distinct_c0, bs.distinct_c1, bs.avg_price\nORDER BY bs.brand_id\n", "recompute": "WITH fans AS (\n SELECT -(e.user_id + 1) AS src, CAST(i.brand_id AS BIGINT) AS dst\n FROM merrec_events e JOIN merrec_items i ON i.item_id = e.item_id\n LEFT JOIN brand_ontology o ON o.brand_id = CAST(i.brand_id AS BIGINT)\n WHERE e.event_type = 'item_like' AND i.brand_id <> 0\n AND (o.brand_id IS NULL OR o.band = 'accept')\n AND e.event_time >= TIMESTAMP '2023-05-01' AND e.event_time < TIMESTAMP '2023-11-01'\n GROUP BY e.user_id, i.brand_id\n), top_brands AS (\n SELECT dst AS brand_id, COUNT(*) AS fans FROM fans GROUP BY dst ORDER BY fans DESC, dst LIMIT 5000\n), similar AS (\n SELECT first, second, similarity\n FROM cugraph_jaccard(\n edges => (SELECT src, dst FROM fans),\n vertex_pairs => (SELECT a.brand_id AS first, b.brand_id AS second\n FROM top_brands a JOIN top_brands b ON a.brand_id < b.brand_id),\n directed => false)\n WHERE similarity >= 0.02\n), worlds AS (\n SELECT vertex AS brand_id, community AS world\n FROM cugraph_leiden(\n edges => (SELECT first AS src, second AS dst, CAST(similarity AS DOUBLE) AS weight FROM similar),\n directed => false, resolution => 3, seed => 42)\n), bridges AS (\n SELECT vertex AS brand_id, value AS betweenness\n FROM cugraph_betweenness_centrality(\n edges => (SELECT first AS src, second AS dst FROM similar),\n directed => false, normalized => true)\n), neighbors AS (\n SELECT first AS brand_id, second AS neighbor FROM similar\n UNION ALL SELECT second, first FROM similar\n), reach AS (\n SELECT n.brand_id, COUNT(*) AS neighbors, COUNT(DISTINCT w.world) AS worlds_reached\n FROM neighbors n JOIN worlds w ON w.brand_id = n.neighbor\n GROUP BY n.brand_id\n), brands AS (\n SELECT CAST(brand_id AS BIGINT) AS brand_id, MIN(brand_name) AS brand_name\n FROM merrec_items WHERE brand_id <> 0 GROUP BY brand_id\n)\nSELECT br.brand_id, b.brand_name, t.fans, w.world, r.neighbors, r.worlds_reached, br.betweenness\nFROM bridges br\nJOIN worlds w ON w.brand_id = br.brand_id\nJOIN reach r ON r.brand_id = br.brand_id\nJOIN top_brands t ON t.brand_id = br.brand_id\nJOIN brands b ON b.brand_id = br.brand_id\nORDER BY br.betweenness DESC, b.brand_name\nLIMIT 120\n", "recompute_canonical": "WITH fans AS (\n SELECT -(e.user_id + 1) AS src, COALESCE(c.canonical_id, CAST(i.brand_id AS BIGINT)) AS dst\n FROM merrec_events e JOIN merrec_items i ON i.item_id = e.item_id\n LEFT JOIN brand_ontology o ON o.brand_id = CAST(i.brand_id AS BIGINT)\n LEFT JOIN brand_canonical c ON c.brand_id = CAST(i.brand_id AS BIGINT)\n WHERE e.event_type = 'item_like' AND i.brand_id <> 0\n AND (o.brand_id IS NULL OR o.band = 'accept')\n AND e.event_time >= TIMESTAMP '2023-05-01' AND e.event_time < TIMESTAMP '2023-11-01'\n GROUP BY 1, 2\n), top_brands AS (\n SELECT dst AS brand_id, COUNT(*) AS fans FROM fans GROUP BY dst ORDER BY fans DESC, dst LIMIT 5000\n), similar AS (\n SELECT first, second, similarity\n FROM cugraph_jaccard(\n edges => (SELECT src, dst FROM fans),\n vertex_pairs => (SELECT a.brand_id AS first, b.brand_id AS second\n FROM top_brands a JOIN top_brands b ON a.brand_id < b.brand_id),\n directed => false)\n WHERE similarity >= 0.02\n), worlds AS (\n SELECT vertex AS brand_id, community AS world\n FROM cugraph_leiden(\n edges => (SELECT first AS src, second AS dst, CAST(similarity AS DOUBLE) AS weight FROM similar),\n directed => false, resolution => 3, seed => 42)\n), bridges AS (\n SELECT vertex AS brand_id, value AS betweenness\n FROM cugraph_betweenness_centrality(\n edges => (SELECT first AS src, second AS dst FROM similar),\n directed => false, normalized => true)\n), neighbors AS (\n SELECT first AS brand_id, second AS neighbor FROM similar\n UNION ALL SELECT second, first FROM similar\n), reach AS (\n SELECT n.brand_id, COUNT(*) AS neighbors, COUNT(DISTINCT w.world) AS worlds_reached\n FROM neighbors n JOIN worlds w ON w.brand_id = n.neighbor\n GROUP BY n.brand_id\n), brands AS (\n SELECT CAST(brand_id AS BIGINT) AS brand_id, MIN(brand_name) AS brand_name\n FROM merrec_items WHERE brand_id <> 0 GROUP BY brand_id\n)\nSELECT br.brand_id, b.brand_name, t.fans, w.world, r.neighbors, r.worlds_reached, br.betweenness\nFROM bridges br\nJOIN worlds w ON w.brand_id = br.brand_id\nJOIN reach r ON r.brand_id = br.brand_id\nJOIN top_brands t ON t.brand_id = br.brand_id\nJOIN brands b ON b.brand_id = br.brand_id\nORDER BY br.betweenness DESC, b.brand_name\nLIMIT 120\n" }, "windows": { "all": { "window": { "start": "2023-05-01", "end": "2023-11-01" }, "stages": [ { "name": "probe", "rows": 120, "elapsed_s": 8.761907765176147, "query_id": "03fde0d5-d09c-4b79-986c-7481db065768", "native_fragments": 15, "host_boundaries": 0, "allocator_peak_bytes": 58471525743, "query_limit_bytes": 96636764160 }, { "name": "context", "rows": 120, "elapsed_s": 0.625574650708586, "query_id": "172341bc-3a23-4c2c-bd4d-91d091d64b50", "native_fragments": 1, "host_boundaries": 0, "allocator_peak_bytes": 11622161774, "query_limit_bytes": 96636764160 }, { "name": "recompute", "rows": 120, "elapsed_s": 8.797936612274498, "query_id": "e1b212ea-c331-47a9-8a69-21e5ac983bca", "native_fragments": 15, "host_boundaries": 0, "allocator_peak_bytes": 56985725519, "query_limit_bytes": 96636764160 }, { "name": "recompute_denylist", "rows": 120, "elapsed_s": 8.79921491490677, "query_id": "e3a88f94-0402-4497-b6ff-ef6efbeaa5bb", "native_fragments": 15, "host_boundaries": 0, "allocator_peak_bytes": 57167449127, "query_limit_bytes": 96636764160 }, { "name": "context_pass2", "rows": 120, "elapsed_s": 0.6289979363791645, "query_id": "b681ac24-5581-4c93-b0b6-5df52d9df528", "native_fragments": 1, "host_boundaries": 0, "allocator_peak_bytes": 11622161582, "query_limit_bytes": 96636764160 }, { "name": "recompute_pass2", "rows": 120, "elapsed_s": 8.770125777926296, "query_id": "c41bc852-d60d-4975-9777-1729edb3d702", "native_fragments": 15, "host_boundaries": 0, "allocator_peak_bytes": 55491218111, "query_limit_bytes": 96636764160 } ], "decisions": [ { "name": "decide", "entities": 120, "distinct_states": 120, "registry_hits": 0, "hit_rate": 0.0, "api_calls": 120, "wall_s": 5.423525845166296, "input_tokens": 127615, "output_tokens": 11974, "cost_usd": 0.00535983, "latency_ms_p50": 577, "latency_ms_p95": 1221, "latency_ms_max": 2211 }, { "name": "decide_pass2", "entities": 120, "distinct_states": 120, "registry_hits": 61, "hit_rate": 0.5083333333333333, "api_calls": 59, "wall_s": 6.267743187956512, "input_tokens": 62460, "output_tokens": 5871, "cost_usd": 0.00262332, "latency_ms_p50": 566, "latency_ms_p95": 1251, "latency_ms_max": 4637 } ], "totals": { "gpu_elapsed_s": 36.38375765737146, "decide_wall_s": 11.691269033122808, "entities": 240, "registry_hits": 61, "api_calls": 179, "input_tokens": 190075, "output_tokens": 17845, "cost_usd": 0.007983150000000001, "max_host_boundaries": 0 }, "passes": [ { "pass": 1, "new_entries": 120, "new_band_counts": { "accept": 63, "review": 30, "reject": 27 }, "registry_hits": 120, "api_calls": 0 }, { "pass": 2, "new_entries": 59, "new_band_counts": { "accept": 34, "review": 13, "reject": 12 }, "registry_hits": 120, "api_calls": 0 } ], "band_counts": { "accept": 97, "review": 43, "reject": 39 }, "kind_counts": { "generic_catchall_or_category": 27, "multi_brand_retailer": 21, "standalone_brand": 117, "superstore_house_brand": 14 }, "ranking": [ { "brand_id": 20430, "name": "Samsung", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.98, "wormhole_pollution": 0.03, "original_rank": 6, "original_betweenness": 0.009053600952029228, "postfilter_rank": 1, "purified_rank": 1, "purified_betweenness": 0.01773890294134617, "betweenness_multiplier": 1.959320168332608, "denylist_rank": 1 }, { "brand_id": 505, "name": "Avon", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.96, "wormhole_pollution": 0.54, "original_rank": 23, "original_betweenness": 0.005223568063229322, "postfilter_rank": 8, "purified_rank": 2, "purified_betweenness": 0.011489967815577984, "betweenness_multiplier": 2.199639724513255, "denylist_rank": 2 }, { "brand_id": 16002, "name": "Nicole Miller", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.85, "wormhole_pollution": 1.62, "original_rank": 18, "original_betweenness": 0.005464966408908367, "postfilter_rank": 5, "purified_rank": 3, "purified_betweenness": 0.010496553964912891, "betweenness_multiplier": 1.9206987160621156, "denylist_rank": 3 }, { "brand_id": 4510, "name": "Conair", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.96, "wormhole_pollution": 0.08, "original_rank": 65, "original_betweenness": 0.003801466664299369, "postfilter_rank": 30, "purified_rank": 4, "purified_betweenness": 0.008835939690470695, "betweenness_multiplier": 2.324350170804196, "denylist_rank": 7 }, { "brand_id": 12843, "name": "Laura Ashley", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.94, "wormhole_pollution": 0.59, "original_rank": 31, "original_betweenness": 0.00449552433565259, "postfilter_rank": 14, "purified_rank": 5, "purified_betweenness": 0.008678542450070381, "betweenness_multiplier": 1.9304850340245274, "denylist_rank": 10 }, { "brand_id": 7265, "name": "Ellen Tracy", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.91, "wormhole_pollution": 0.34, "original_rank": 15, "original_betweenness": 0.005698487162590027, "postfilter_rank": 4, "purified_rank": 6, "purified_betweenness": 0.008388789370656013, "betweenness_multiplier": 1.4721081457772758, "denylist_rank": 9 }, { "brand_id": 10627, "name": "IZOD", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.94, "wormhole_pollution": 0.09, "original_rank": 8, "original_betweenness": 0.007952889427542686, "postfilter_rank": 2, "purified_rank": 7, "purified_betweenness": 0.00820834282785654, "betweenness_multiplier": 1.0321208288687078, "denylist_rank": 5 }, { "brand_id": 2854, "name": "Bella", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.78, "wormhole_pollution": 1.76, "original_rank": null, "original_betweenness": null, "postfilter_rank": null, "purified_rank": 8, "purified_betweenness": 0.008026077412068844, "betweenness_multiplier": null, "denylist_rank": 12 }, { "brand_id": 2571, "name": "Black & Decker", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.95, "wormhole_pollution": 1.12, "original_rank": 66, "original_betweenness": 0.0037961988709867, "postfilter_rank": 31, "purified_rank": 9, "purified_betweenness": 0.007740165572613478, "betweenness_multiplier": 2.0389252079940876, "denylist_rank": 6 }, { "brand_id": 8910, "name": "Gibson", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.75, "wormhole_pollution": 1.16, "original_rank": 10, "original_betweenness": 0.007044518832117319, "postfilter_rank": 3, "purified_rank": 10, "purified_betweenness": 0.0077077122405171394, "betweenness_multiplier": 1.0941431805641848, "denylist_rank": 8 }, { "brand_id": 4184, "name": "Charming Charlie", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.92, "wormhole_pollution": 0.57, "original_rank": 19, "original_betweenness": 0.005450394004583359, "postfilter_rank": 6, "purified_rank": 11, "purified_betweenness": 0.007215810474008322, "betweenness_multiplier": 1.323906210806115, "denylist_rank": null }, { "brand_id": 4741, "name": "Crayola", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.97, "wormhole_pollution": 0.12, "original_rank": 26, "original_betweenness": 0.004831797908991575, "postfilter_rank": 10, "purified_rank": 12, "purified_betweenness": 0.007092369720339775, "betweenness_multiplier": 1.4678531374711394, "denylist_rank": 13 }, { "brand_id": 18651, "name": "Rue21", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.8, "wormhole_pollution": 0.41, "original_rank": 41, "original_betweenness": 0.0042989300563931465, "postfilter_rank": 20, "purified_rank": 13, "purified_betweenness": 0.0070745437406003475, "betweenness_multiplier": 1.645652208292957, "denylist_rank": null }, { "brand_id": 24792, "name": "Warner Bros.", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.91, "wormhole_pollution": 0.77, "original_rank": 27, "original_betweenness": 0.004803807474672794, "postfilter_rank": 11, "purified_rank": 14, "purified_betweenness": 0.006586064584553242, "betweenness_multiplier": 1.3710092711410846, "denylist_rank": 15 }, { "brand_id": 12700, "name": "L'Oreal", "band": "accept", "entity_kind": "standalone_brand", "is_standalone_brand": 0.98, "wormhole_pollution": 0.03, "original_rank": 21, "original_betweenness": 0.00537595059722662, "postfilter_rank": 7, "purified_rank": 15, 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