Assess whether a split between models is a review queue or noise (a2a5dd)

Situation

The Lifecycle and Accountability desk packet is output-scoring rubric that never fails a high-risk output after a purchase order signed before eval data rights were granted. Vendor-contract AI counsel's operations partner in a manufacturer sunsetting a vision model still used in QA must name A split between models is a review queue or Noise for this AI Governance Layer file. Vendor-contract AI counsel's operations partner in a manufacturer sunsetting a vision model still used in QA has to name A split between models is a review queue or Noise for this AI Governance Layer Lifecycle and Accountability file.

Decision

Vendor-contract AI counsel's operations partner in a manufacturer sunsetting a vision model still used in QA must choose A split between models is a review queue / Noise using output-scoring rubric that never fails a high-risk output after a purchase order signed before eval data rights were granted.

Hypotheses to test

• Output-scoring rubric that never fails a high-risk output reads as A split between models is a review queue once a purchase order signed before eval data rights were granted is lined up to the same AI Governance Layer population.
• Output-scoring rubric that never fails a high-risk output is closer to Noise after a purchase order signed before eval data rights were granted; A split between models is a review queue would over-claim this Lifecycle and Accountability extract.
• A dual reading is still live in output-scoring rubric that never fails a high-risk output for vendor-contract AI counsel's operations partner in a manufacturer sunsetting a vision model still used in QA.
• Output-scoring rubric that never fails a high-risk output is missing the fact vendor-contract AI counsel's operations partner needs after a purchase order signed before eval data rights were granted; stop this AI Governance Layer close.

Analysis required

• Map the control-plane score in output-scoring rubric that never fails a high-risk output to the policy gate vendor-contract AI counsel's operations partner can enforce.
• Name the override that would let a split between models proceed without a silent bypass.
• Test whether a purchase order signed before eval data rights were granted changed routing, logging, or human-in-the-loop on the live agent path.
• For this AI Governance Layer Lifecycle and Accountability file, read output-scoring rubric that never fails a high-risk output against a purchase order signed before eval data rights were granted and write the one fact that would move a split between models for vendor-contract AI counsel's operations partner.