DETERMINATOR

Unified Sandbox

1. Silhouette Engine

Data Target Input
UPLINK STREAM PREVIEW
Contour Calibration
DETECTOR MASK & CONTOURS
NO TARGET DETECTED
Extraction Metrics
Bounding Box -
Contour Points 0
Detected Holes 0
Processing Load 0.0 ms

2. Morphogenesis & Research

FR 00 FR 20
CAUSAL TRANSITION NETWORK (HOVER NODES)
Luminance Threshold Model
Y = c_R * R + c_G * G + c_B * B
Converts raw RGB sensors to grayscale luminance. The Rec. 709 coefficient standard uses: c_R = 0.2126, c_G = 0.7152, c_B = 0.0722.
Red Weight (c_R) 0.2126
Green Weight (c_G) 0.7152
Blue Weight (c_B) 0.0722
Marching Squares Contour Tracking
CellIndex = (V_0 << 3) | (V_1 << 2) | (V_2 << 1) | V_3
Uses a 4-bit binary code generated from cell corner values to lookup boundary paths. Unlike the original Row Extrema model, this captures arbitrary curves and inner voids (holes).
Topological Compactness
Compactness = Area_bbox / Count_points
Measures structural complexity. High values denote complex manifolds. Winding area: Area = 0.5 * ∑ (x_i * y_i+1 - x_i+1 * y_i).
Bayesian Belief Update
P(H | E) = (P(H) + P(E | H)) / 2
Calibrates research hypothesis confidence scores using structural evidence gathered across time frames.
Deterministic Confidence Attribution
Confidence = w_N * Novelty + w_S * Surprise + w_P * Persistence + w_St * Stability
Computes overall explainability scores using weighted attribute metrics.
Novelty Weight (w_N) 0.30
Surprise Weight (w_S) 0.25
Persistence Weight (w_P) 0.25
Stability Weight (w_St) 0.20
System Log

3. Explainability

Dataset Integrity
Dataset Hash Fingerprint ffffffff
Feature Attribution & Anomaly Drivers
■ Novelty ■ Surprise ■ Persistence ■ Stability
Anomaly Attribution details
Traceability Chain
Segmentation / ExtractionOK
Shape RegistrationOK
ICP Topological MatchingOK
ScaleSpace Feature AnalysisOK
Semantic Memory ArchivingOK
Causal Morphogenesis GraphOK
Bayesian Research EngineOK
Report Exports