# Stage 57: 0..100 Calibrated Scores (aq57_score_confidence_probability_0_100 / DB-UNIT-09)

Calibrates four distinct 0..100 scales (confidence, strength, probability, likelihood) via 0-call PAVA isotonic maps and log-odds Bayes factors.

- Atomic micro-queue: `aq57_score_confidence_probability_0_100` (visibility: `public_commons`)
- Backing database unit: `DB-UNIT-09` (`Agent Fees, AFK Budget Holds, CPOP Review & Q12 Royalty Clearinghouse`)
- Database cell: `ledger-db.teleon.dev` (`teleon_core.agent_fee_tariffs`, `teleon_core.agent_fee_quotes_and_holds`, `teleon_core.agent_fee_settlement_splits`, `teleon_core.agent_fee_savings_evidence`, `teleon_core.agent_fee_csr_tax_ledger`, `teleon_core.cpop_orthogonal_review_verdicts`, `teleon_core.teleon_double_entry_royalty_journal`)
- Claim row hydration: `single_self_contained_flat_task_row` (100% context inline, normalized writeback)
- Track 2 Sister queue (`SISTER-09`): `sq09-improve-ledger-queue.teleon.dev` (Tune off-peak AFK discounts, Shapley/Q12 attribution weights, Hebbian decay, and CUSUM drift windows.)
- Lanes: Confidence & Strength, Probability 0..100, Likelihood Ratio

## Execution engines & AI Agent freedom contract

- **Deterministic (0 model calls)**: `q12_ledger_and_cusum_engine_v1` (PAVA isotonic calibrator, Brier/ECE & Bayes-factor combiner)
- **Hybrid (<=1 model call)**: `cpop_evidence_audit_summarizer_v1`
- **AI Agent & Bot Fleet (1..6 calls)**: `cross_family_independent_reviewer_v1` (Reference-class prior & evidential-support estimator)
  - Contract: **Instructions + Resources + Freedom to search for new resources and new ways not bound by instructions**

## Database invariants

- Ledger and fee-hold rows run under SERIALIZABLE ACID_COMMIT_KERNEL_POLICY with zero inline agent middleware.
- A producer family never approves its own work (reviewer_family != producer_family).

## External & internal bot fleet actions

- Keep outcome probability_0_100 strictly separate from epistemic confidence_0_100 sample support
- Score causal lift over bare_baseline as strength_0_100 and prior-free support as likelihood_0_100
- Update per-domain PAVA isotonic calibration step functions against realized outcomes

## Sister self-tuning areas

- Tune off-peak AFK congestion discounts and 6-bucket Q12 royalty weights (DIM-08)
- Calibrate Hebbian learning rate and CUSUM drift sensitivity on historical anomalies (DIM-01)
- Add deterministic Sybil-collusion graph detectors (DIM-06)