Koshe, A. | bioRxiv Science

bioRxiv2026

Composite Certainty: Addressing Metric Degeneracy in Parameter Inference for Model-Based Diagnostics

Quantifying the diagnostic dispersion of inferred parameter distributions is a challenge in uncertainty-aware modeling. Scalar summaries such as credible interval width are topology-blind; fundamentally different posterior morphologies can yield identical scores, obscuring whether a parameter is precisely estimated or constrained to a range. We propose a Composite Certainty Framework that addresses this metric degeneracy by aggregating five complementary uncertainty metrics including interquartile range, standard deviation, full width at half maximum, Shannon entropy, and mass width. These metrics are aggregated through non-parametric Borda rank voting into a single, unitless consensus certainty score. Applied to a simulation-based inference pipeline for a finite-element model of the human middle ear tuned to cadaveric acoustic measurements, the framework reveals parameter-specific identifiability profiles invisible to any individual metric. It produces two actionable clinical thresholds: (1) the maximum tolerable measurement noise for reliable parameter recovery, and (2) the minimum simulation budget for posterior convergence. We demonstrated that no single metric captures all aspects of posterior dispersion, as spread-based metrics and entropy diverge systematically for clinically critical parameters, whereas their aggregation produces a consensus reflecting genuine diagnostic certainty. The framework is generalizable to any model-based diagnostic pipeline where posterior distribution not merely its coverage, but determines clinical certainty.

bioengineering↗

bioRxiv2026

Composite Biofidelity: Addressing Metric Degeneracy in Biomechanical Model Validation and Machine Learning Loss Design

Spectral similarity is often judged with a single metric such as RMSE, yet this can be misleading: physically different errors can produce similar scores. This is a critical limitation for computational biomechanics, where spectral agreement underpins both model validation and machine-learning loss design. Here, we develop a multi-metric framework for objective spectral biofidelity and test whether it better captures meaningful disagreement across complex frequency-domain responses. We evaluated 12 complementary similarity metrics, including CORA and ISO/TS 18571, using controlled spectral perturbations that mimic common real-world deviations such as resonance shifts, localized spikes, and broadband tilts. We then applied the framework to an SBI-tuned finite-element middle-ear model to assess convergence with training dataset size and robustness to measurement noise across repeated stochastic runs. No single metric performed reliably across all distortion types. Shape-based metrics tracked resonance morphology but could miss vertical scaling, whereas MaxError remained important for narrowband anomalies that smoother metrics underweighted. CORA and ISO 18571 did not consistently outperform simpler metrics. Rank aggregation using Borda count provided a robust consensus across metrics, enabling objective identification of training-data saturation and noise thresholds beyond which similarity rankings became unstable. These results show that spectral biofidelity cannot be reduced to a single norm. A multi-metric consensus provides a clearer and more physically meaningful basis for comparing experimental and simulated spectra, and offers a more defensible foundation for data-fidelity terms in physics-informed and simulation-based machine learning.

bioengineering↗