Bhārata Strata · Maharashtra · Parbhani · morphological twins
Places like cell 4890000727
The MH-36 locality cells most similar to this Parbhani 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
0.042
lower = more alike (weighted)
The twins, closest first
#1 · Parbhanicell 4890000669
distance 0.042
#2 · Parbhanicell 4890000512
distance 0.055
#3 · Parbhanicell 4890000785
distance 0.059
#4 · Parbhanicell 4890000615
distance 0.065
#5 · Parbhanicell 4890000841
distance 0.069
#6 · Parbhanicell 4890000670
distance 0.07
#7 · Parbhanicell 4890000725
distance 0.092
#8 · Parbhanicell 4890000560
distance 0.096
#9 · Parbhanicell 4890000561
distance 0.096
#10 · Nandedcell 4850003337
distance 0.098 · cross-district
#11 · Laturcell 4810005495
distance 0.102 · cross-district
#12 · Parbhanicell 4890000513
distance 0.103
#13 · Parbhanicell 4890000726
distance 0.103
#14 · Parbhanicell 4890000782
distance 0.104
#15 · Parbhanicell 4890000840
distance 0.104
#16 · Nandedcell 4850001528
distance 0.107 · cross-district
#17 · Laturcell 4810005899
distance 0.11 · cross-district
#18 · Laturcell 4810005960
distance 0.111 · 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":489,"cluster_id":4890000727}}}'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.