Symbolic regression promises readable equations but struggles to encode unit-aware thresholds and conditional logic. We propose logistic-gated operators (LGO) -- differentiable gates with learnable location and steepness -- embedded as typed primitives and mapped back to physical units for audit. Across two primary health datasets (ICU, NHANES), the hard-gate variant recovers clinically plausible cut-points: 71% (5/7) of assessed thresholds fall within 10% of guideline anchors and 100% within 20%, while using far fewer gates than the soft variant (ICU median 4.0 vs 10.0; NHANES 5.0 vs 12.5), and remaining within the competitive accuracy envelope of strong SR baselines. On predominantly smooth tasks, gates are pruned, preserving parsimony. The result is compact symbolic equations with explicit, unit-aware thresholds that can be audited against clinical anchors -- turning interpretability from a post-hoc explanation into a modeling constraint and equipping symbolic regression with a practical calculus for regime switching and governance-ready deployment.

翻译：符号回归虽能提供可读方程，但在编码单位感知阈值与条件逻辑方面存在困难。我们提出逻辑门控算子——一种具有可学习位置与陡度的可微分门控结构——将其作为类型化基元嵌入，并映射回物理单位以实现审计。在两个主要健康数据集（ICU、NHANES）上的实验表明，硬门控变体能恢复具有临床合理性的截断点：评估阈值中71%（5/7）落在指南锚点的10%误差范围内，100%落在20%误差范围内，且所用门控数量远少于软门控变体（ICU中位数4.0对10.0；NHANES中位数5.0对12.5），同时保持在强符号回归基线的竞争性精度范围内。在主要平滑任务中，门控会被剪枝以保持简洁性。最终得到具有显式单位感知阈值的紧凑符号方程，这些阈值可对照临床锚点进行审计——将可解释性从事后解释转变为建模约束，并为符号回归提供了适用于状态切换与合规部署的实用计算框架。