Bhārata Strata · Maharashtra · Parbhani · morphological twins
Places like cell 4890000669
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 4890000727
distance 0.042
#2 · Parbhanicell 4890000785
distance 0.058
#3 · Parbhanicell 4890000841
distance 0.06
#4 · Parbhanicell 4890000512
distance 0.066
#5 · Parbhanicell 4890000561
distance 0.071
#6 · Parbhanicell 4890000670
distance 0.071
#7 · Parbhanicell 4890000615
distance 0.075
#8 · Parbhanicell 4890000560
distance 0.079
#9 · Parbhanicell 4890000513
distance 0.084
#10 · Parbhanicell 4890000725
distance 0.087
#11 · Nandedcell 4850003337
distance 0.097 · cross-district
#12 · Laturcell 4810006049
distance 0.103 · cross-district
#13 · Nandedcell 4850001603
distance 0.104 · cross-district
#14 · Laturcell 4810005500
distance 0.104 · cross-district
#15 · Laturcell 4810005495
distance 0.105 · cross-district
#16 · Parbhanicell 4890000782
distance 0.107
#17 · Parbhanicell 4890000840
distance 0.108
#18 · Parbhanicell 4890000327
distance 0.11
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":4890000669}}}'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.