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Degradation-kernel challenger study: 2026-07-20

This study tests whether flexible degradation distributions improve on the frozen three-component competing-risk model. The incumbent uses a constant per-base degradation hazard. Challengers were deliberately bounded:

  • piecewise2: constant plus two regularized directional shapes;
  • piecewise3: constant plus four regularized directional/central shapes;
  • beta: length-dependent mean retained fraction and concentration 2, 5, or 10.

Piecewise models embed the constant model and penalize squared log-rate departures. All models retain the same intact and length-invariant technical components, cross-fitting, degradation posterior, evidence gate, reliability gate, and Bayes-factor cap. No model was tuned after examining abundance truth.

Endpoint fit and stability

Across all 15 PRJEB53210 degradation libraries:

Kernel Held-out log density Mean hazard fold SD Selected shapes
constant -2.22634 0 constant
piecewise2 -2.21159 0 0.67, 1.33
piecewise3 -2.20325 0 0.67, 1.00, 1.33
beta -2.11577 0.0098 concentration 2 or 5

All flexible models fit held-out endpoints better, and piecewise hazards are stable across folds. This does not translate monotonically into quantification accuracy: beta is the clearest counterexample.

LongBench

Values are means across eight 50,000-read ONT direct-RNA libraries.

Kernel CCC Pearson RMSE MARD Coverage time
constant 0.497474 0.712606 6257.00 0.449311 0.100 s
piecewise2 0.497413 0.712542 6257.46 0.449320 0.203 s
piecewise3 0.497388 0.712519 6257.73 0.449321 0.313 s
beta 0.496288 0.711223 6267.30 0.449427 2.104 s

Piecewise regressions are very small but consistent in the aggregate. Beta is both less accurate and roughly 21 times slower than constant at this depth.

At 250,000 HCC827 reads, piecewise2 is stable and slightly better than constant:

Kernel CCC Pearson RMSE Coverage time
constant 0.562581 0.739437 6869.19 0.426 s
piecewise2 0.562617 0.739453 6868.54 0.919 s

The gain is only 0.000035 CCC and does not offset the complete-panel result or the twofold runtime cost.

Degradation trajectories

Kernel TS10 Pearson TS12 7.2 Pearson TS12 7.3 Pearson
constant 0.96500 0.94876 0.94013
piecewise2 0.96681 0.94833 0.93930
piecewise3 0.96671 0.94835 0.93930
beta 0.96439 0.94831 0.93885

Piecewise hazards improve TS10 but regress both TS12 endpoints. There is no uniform robustness winner beyond the incumbent.

Frozen truth and large-sample runtime

Kernel Synthetic CCC Synthetic RMSE SIRV CCC Source CCC Synthetic coverage time
constant 0.996926 24.092 0.671305 0.983387 2.77 s
piecewise2 0.996907 24.167 0.671326 0.983387 7.55 s
piecewise3 0.996907 24.166 0.671255 0.983387 12.69 s
beta 0.994516 32.118 0.640705 0.983387 63.97 s

The piecewise differences are small, but neither improves the large synthetic truth. Beta decisively fails truth and runtime gates despite its much better held-out endpoint density.

Parameter-recovery checks

Deterministic simulations verify that the regularized piecewise2 challenger collapses to constant hazard for constant data and recovers the direction of a simulated 0.67/1.33 regional hazard. Thus its negative benchmark result is not caused by a nonfunctional implementation.

Decision

Keep constant as the default and validated kernel. Retain only the bounded piecewise2 challenger behind the explicitly experimental --degradation-kernel constant|piecewise2 option. The piecewise3 and beta code was removed after evaluation to avoid carrying unjustified complexity. Do not automatically select a kernel from endpoint likelihood: the beta experiment proved that this would select a worse quantification model. Piecewise2 is the only credible future challenger, but its current gains are dataset-specific and too small to justify replacement.

Artifacts are under oarfish-evaluation-data/degradation/kernel-* and oarfish-evaluation-data/longbench/kernel-*.