Calibration Methods and Transfer for UCBD Uncertainty Scores

Develop and evaluate calibration techniques for UCBD-derived uncertainty scores—including isotonic regression, temperature scaling, and cross-dataset calibration transfer—and determine their effectiveness and transferability beyond Platt scaling.

Background

While Platt scaling substantially improved calibration (ECE reduction by 88%) in the reported experiments, the authors did not investigate other standard calibration techniques or their transferability across datasets.

Explicitly noting that these options are not yet explored indicates an unresolved methodological question about achieving robust and transferable calibration for uncertainty estimates in this setting.

References

Isotonic regression, temperature scaling, and cross-dataset calibration transfer not yet explored.

— The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation  (2603.24124 - Liu, 25 Mar 2026) in Limitations (8)

Neither tested uncertainty construction is calibrated and no conformal or external calibration layer is evaluated: a layer fitted on the 307 development cases would be invalid because those cases already determined the fits and the selected configurations, and CARE2026 and MM-WHS are too small at 58 and 20 cases to spare an independent calibration partition.

— A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model  (2608.19932 - Gazda et al., 20 Aug 2026) in Discussion, Section 'Limitations'

We stress that this is a within-distribution result: the map is scenario-specific, and its transportability to a genuinely new target domain is not established and would require separate evaluation there.

— Multi-Method Causal Evidence Synthesis: Ranking Candidate Drivers by Convergent Cross-Method Evidence from Observational Data  (2608.20187 - Gupta et al., 20 Aug 2026) in Section 5.2, “Calibration”; also Section 6.2, Limitations