Bhārata Strata · Maharashtra · Hingoli · morphological twins
Places like cell 4770001472
The MH-36 locality cells most similar to this Hingoli 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 3 distinct districts
Index confidence
0.92
GUM mean confidence of the anchor (R24)
Dimensions compared
45
morphology-v2 feature dimensions
Closest twin distance
0.082
lower = more alike (weighted)
The twins, closest first
#1 · Hingolicell 4770001060
distance 0.082
#2 · Hingolicell 4770001473
distance 0.106
#3 · Hingolicell 4770001061
distance 0.144
#4 · Hingolicell 4770001414
distance 0.154
#5 · Hingolicell 4770001471
distance 0.161
#6 · Parbhanicell 4890002532
distance 0.183 · cross-district
#7 · Yavatmalcell 5000001069
distance 0.193 · cross-district
#8 · Hingolicell 4770002078
distance 0.194
#9 · Hingolicell 4770001529
distance 0.201
#10 · Hingolicell 4770002404
distance 0.205
#11 · Hingolicell 4770000104
distance 0.207
#12 · Hingolicell 4770000598
distance 0.207
#13 · Hingolicell 4770002200
distance 0.208
#14 · Yavatmalcell 5000001181
distance 0.214 · cross-district
#15 · Hingolicell 4770000547
distance 0.215
#16 · Parbhanicell 4890002614
distance 0.216 · cross-district
#17 · Parbhanicell 4890002956
distance 0.217 · cross-district
#18 · Hingolicell 4770001071
distance 0.217
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":477,"cluster_id":4770001472}}}'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.