Bhārata Strata · Maharashtra · Buldhana · morphological twins
Places like cell 4720003443
The MH-36 locality cells most similar to this Buldhana cell across terrain, built form, land cover, climate dynamics and connectivity — ranked by a GUM-confidence-weighted (R24) distance, with the temporal block down-weighted. Each twin is a real cell with its own witnessed dossier.
Twins found
18
across 2 distinct districts
Index confidence
0.93
GUM mean confidence of the anchor (R24)
Dimensions compared
45
morphology-v2 feature dimensions
Closest twin distance
0.073
lower = more alike (weighted)
The twins, closest first
#1 · Buldhanacell 4720003750
distance 0.073
#2 · Buldhanacell 4720003826
distance 0.086
#3 · Akolacell 4670000152
distance 0.087 · cross-district
#4 · Buldhanacell 4720005024
distance 0.092
#5 · Buldhanacell 4720003521
distance 0.098
#6 · Buldhanacell 4720003970
distance 0.101
#7 · Buldhanacell 4720004880
distance 0.101
#8 · Akolacell 4670000286
distance 0.101 · cross-district
#9 · Buldhanacell 4720003440
distance 0.102
#10 · Akolacell 4670000237
distance 0.106 · cross-district
#11 · Buldhanacell 4720003359
distance 0.106
#12 · Buldhanacell 4720003973
distance 0.106
#13 · Buldhanacell 4720005237
distance 0.108
#14 · Akolacell 4670000208
distance 0.111 · cross-district
#15 · Buldhanacell 4720005821
distance 0.111
#16 · Akolacell 4670000871
distance 0.111 · cross-district
#17 · Buldhanacell 4720004806
distance 0.111
#18 · Buldhanacell 4720003976
distance 0.112
witness: find_twins — morphology-twin-index-v2 (R24, GUM-confidence-weighted, magnitude-aware, temporal block down-weighted). Distance is the weighted feature distance; lower is more alike. Cross-district twins are the discriminative signal.
Re-witness these twins
curl -s https://api.gridrock.ai/mcp -H 'Content-Type: application/json' \
-H 'Accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"find_twins",
"arguments":{"admin_code":472,"cluster_id":4720003443}}}'Scope: the live MH-36 cube. Twins are computed from the sha-verified morphology-v2 vector; the engine supplies no goodness/suitability ranking — only morphological similarity.