Bhārata Strata · Maharashtra · Parbhani · morphological twins
Places like cell 4890001946
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
0.096
lower = more alike (weighted)
The twins, closest first
#1 · Parbhanicell 4890001691
distance 0.096
#2 · Parbhanicell 4890002040
distance 0.121
#3 · Hingolicell 4770000242
distance 0.126 · cross-district
#4 · Parbhanicell 4890002445
distance 0.127
#5 · Parbhanicell 4890002359
distance 0.13
#6 · Parbhanicell 4890002130
distance 0.144
#7 · Parbhanicell 4890002529
distance 0.146
#8 · Parbhanicell 4890001957
distance 0.148
#9 · Hingolicell 4770000149
distance 0.157 · cross-district
#10 · Parbhanicell 4890002277
distance 0.157
#11 · Parbhanicell 4890001789
distance 0.159
#12 · Parbhanicell 4890001958
distance 0.162
#13 · Beedcell 4700004667
distance 0.169 · cross-district
#14 · Parbhanicell 4890002955
distance 0.169
#15 · Parbhanicell 4890002768
distance 0.172
#16 · Parbhanicell 4890001790
distance 0.172
#17 · Beedcell 4700000710
distance 0.173 · cross-district
#18 · Beedcell 4700005725
distance 0.174 · 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":4890001946}}}'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.