Bhārata Strata · Maharashtra · Buldhana · morphological twins
Places like cell 4720005686
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 5 distinct districts
Index confidence
0.93
GUM mean confidence of the anchor (R24)
Dimensions compared
45
morphology-v2 feature dimensions
Closest twin distance
0.084
lower = more alike (weighted)
The twins, closest first
#1 · Buldhanacell 4720005906
distance 0.084
#2 · Akolacell 4670001053
distance 0.154 · cross-district
#3 · Akolacell 4670001683
distance 0.162 · cross-district
#4 · Buldhanacell 4720006133
distance 0.163
#5 · Buldhanacell 4720005033
distance 0.165
#6 · Beedcell 4700005585
distance 0.167 · cross-district
#7 · Buldhanacell 4720006151
distance 0.179
#8 · Buldhanacell 4720006128
distance 0.18
#9 · Buldhanacell 4720004793
distance 0.181
#10 · Jalnacell 4790003073
distance 0.182 · cross-district
#11 · Buldhanacell 4720006561
distance 0.182
#12 · Buldhanacell 4720005980
distance 0.184
#13 · Buldhanacell 4720004942
distance 0.184
#14 · Buldhanacell 4720005905
distance 0.184
#15 · Buldhanacell 4720004589
distance 0.184
#16 · Buldhanacell 4720005473
distance 0.185
#17 · Buldhanacell 4720005304
distance 0.186
#18 · Hingolicell 4770003740
distance 0.187 · cross-district
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":4720005686}}}'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.