Audio-Visual Conflict Report

Audio-Visual Conflict Report — Multi-Modal Verification

The Audio-Visual Conflict Report captures and documents instances where the Digital Refinery's visual classification (camera-based AI) contradicts information provided through the driver audio channel. This semantic conflict triggers an automatic escalation to ensure accurate waste characterization.

In practice, these conflicts arise when a driver verbally identifies a load as clean construction wood, but the visual AI detects features consistent with treated lumber — or vice versa. The multi-modal Gemma 4 architecture processes both RGB imagery and audio transcription simultaneously, applying semantic analysis to detect contradictions before the load proceeds through the pipeline.

The report documents: visual classification result and confidence, audio transcript with driver statements, semantic analysis of the conflict, risk classification based on conflict severity, recommended resolution pathway (re-inspect, XRF test, or reject), and resolution outcome with final classification. This creates an auditable record showing that conflicts were identified and resolved systematically rather than overlooked.

Audio-Visual Conflicts are a significant source of contamination risk in the Ontario construction waste industry. Drivers frequently misidentify treated wood — particularly pressure-treated lumber that has been painted, stained, or weathered — as clean material. The Digital Refinery's multi-modal approach catches these discrepancies at the intake point rather than at the hub, preventing contaminated material from entering the clean processing stream and avoiding CIRCIL Levy exposure for both 905WOOD and the client.