The shadow model judged by the old model’s evidence
BrambleGrid is a fictional grid-asset inspection company. Regional electric utilities hire it to combine drone imagery, asset records, and certified field inspection so scarce crews visit pole-top equipment that most needs a closer look.
A drone could photograph thousands of connector assemblies in a morning. A physical visit was different. It needed access coordination, a qualified crew, and enough time to perform the same close-range inspection protocol on every asset. BrambleGrid had capacity for 240 flexible visits per week. Twenty always remained reserved for manual safety reports that did not come from a model.
That made field inspection both an operating resource and an evidence resource.
BrambleGrid's ML-assisted product was called Spanwatch. For one recent connector evidence snapshot, Spanwatch estimated the probability that a protocol-C3 inspection within seven days would find an actionable connector condition. It did not predict an outage, and it could not authorize a repair.
Dalia Moravec (the ML reliability engineer responsible for calibration, evaluation cohorts, and launch recommendations) owned Spanwatch's model evidence.
Jon Ibarra (the field-inspection planning lead responsible for the weekly 240-visit capacity ledger) owned which proposed visits entered the field workflow.
Mara Venn (the lead asset-integrity engineer responsible for protocol-C3 adjudication and label revision) owned the final condition record. A utility duty engineer—not Spanwatch, Dalia, Jon, or Mara—separately decided whether to restrict or repair an asset.
The boundaries looked fussy until the week BrambleGrid tried to replace its model.