Evidence item

The PubMed abstract directly supports the model's reported RMSE, baseline improvement, and unseen-geometry generalization. DIRECT_TEXT

Authenticity UNVERIFIED · extraction UNVERIFIED · independence group none (unreviewed) · created seq 16997

Source: Shadow fading prediction at 18 GHz through physics guided learning in vegetative corridors · locator {} · excerpt hash sha256:f9aa1ebb7851cd5c4fe3de1514be80946477eb1c93747794deeaf5de36c53c57

Exact excerpt

Under leave-one-scenario-out cross-validation, the hybrid model achieves RMSE = 2.97 dB, a 24.0% improvement over the CI baseline. Crucially, while pure ensemble methods (Random Forest, Gradient Boosting, XGBoost) exhibit performance degradation up to 1.05 dB when extrapolating to unseen geometric configurations, the hybrid architecture demonstrates a 0.35 dB improvement, achieving better accuracy on novel corridor configurations than on interpolated data.

Linked claims

Claim	Direction	Relevance	Steps	Counted
dd2af2b0	SUPPORT	DIRECT	0	yes

Audits

None.