CFIL
Confidence Filter Engines
1. Overview
Confidence Filter Engines (CFIL) are micro-engines responsible for assigning confidence scores to data signals and filtering or flagging those signals based on explicit confidence thresholds.
CFIL engines provide a deterministic control mechanism for deciding whether data is admissible for downstream use.
CFIL is an engine category within the MICE taxonomy. Concrete implementations are registered as micro-engine instances (MEIDs) that implement the CFIL type.
2. Design Principles
-
Deterministic Scoring
Confidence scores are derived using explicit, reproducible rules. -
Threshold-Based Control
Acceptance or rejection is governed by declared thresholds. -
Non-Interpretive
CFIL engines do not infer meaning or correct data; they evaluate confidence only.
3. Scope of Responsibility
3.1.What CFIL Engines Do
- Assign confidence scores to signals
- Combine multiple confidence indicators
- Filter or flag data below defined thresholds
- Mark signals as provisional or restricted
Typical inputs include:
- Extraction confidence scores
- Sensor quality indicators
- Data completeness metrics
4. What CFIL Engines Do Not Do
- ❌ Modify or compute values (CALC)
- ❌ Transform units or formats (TRFM)
- ❌ Estimate missing data (SEM)
- ❌ Perform policy enforcement (TRPG / ZADIF)
CFIL engines evaluate trustworthiness, not correctness.
5. Inputs
CFIL engines consume:
- Signals with associated confidence metadata
- Confidence scoring rules
- Threshold definitions
Inputs may originate from:
- EXTR
- SENS
- TSER
- NLPI (assistive outputs, when approved)
6. Outputs
CFIL engines produce:
- Confidence-scored signals
- Acceptance or rejection flags
- Confidence metadata and provenance references
Outputs may be consumed by:
- VALI
- AGGR
- Reporting and disclosure workflows
7. Audit & Provenance
Every CFIL execution records:
- Confidence scoring rules applied
- Thresholds used
- Resulting confidence score
- Filtering decisions
This supports:
- auditability,
- explainability,
- verifier review of data admissibility.
8. Canonical Identification
- Engine Type:
CFIL - USO Code:
CFIL - Category: Micro Engine (MICE-adjacent)
- Layer: Computation Hub
9. Design Rationale
Separating confidence evaluation into a dedicated engine category ensures that data trust decisions are explicit, transparent, and reproducible.
Confidence is treated as a first-class signal attribute, not an implicit assumption.
10. Related Engine Types
- EXTR — Extraction Engines
- SENS — Sensor Logic Units
- TSER — Time-Series Engines
- VALI — Validation Engines
Status: Stable
Owner: Computation Hub / MICE