发表机构
Data Science and AI Division, Azyri; Universidad Autónoma de Ciudad Juárez; Pennsylvania State University; Universitat Jaume I（Azyri公司数据科学与人工智能部门; 华雷斯自治大学; 宾夕法尼亚州立大学; 豪梅一世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种不确定性感知深度学习框架，结合多技术与集成模型，实现史前手部模板的性别归因，同时量化不确定性，提升考古推断的稳健性与可重复性。
AI 中文摘要
确定旧石器时代晚期手部模板创作者的生物性别仍是一项具有挑战性的问题，原因在于缺乏真实标签、当代人群与史前人群存在差异，以及图像退化带来的不确定性。传统形态测量方法存在不同性别间结构重叠度高、跨人群泛化能力差、特征工程主观性强等问题。本研究提出一种用于史前手部模板性别归因的不确定性感知深度学习框架，该框架在整个分析流程中显式建模、传播并聚合不确定性。其方法结合了双图像处理、双轮廓提取、结构化轮廓增强、模型架构多样性以及基于集成的决策聚合。该流程为每个模板生成12种合理的轮廓实现以捕捉边界不确定性，这些轮廓由两个各含10个深度神经网络的集成模型（EfficientNet-B3和MobileViT-S）处理，这些模型在14036个当代手部样本上训练而成。此外，三角验证方案将集成预测与无监督二维潜在空间流形映射（UMAP + k-NN）及可解释AI空间归因（LayerCAM）相结合，以确保解剖学一致性。在当代数据上，集成模型实现了强劲的分类性能，在年龄较大的组中准确率超过88%。当应用于史前模板时，该框架同时输出性别预测和内部一致性的置信度指标，从而能够区分形态稳定案例与模糊案例。集成预测、潜在空间结构及可解释性分析的一致性表明，不确定性可成为考古推断的可测量组成部分，实现对古代岩石艺术的稳健且可重复的解码。
英文摘要
Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of ground truth, population differences between contemporary and prehistoric groups, and the uncertainty introduced by image degradation. Traditional morphometric methods suffer from high structural overlap across sexes, poor cross-population generalizability, and subjective feature engineering. This study presents an uncertainty-aware deep learning framework for sex attribution in prehistoric hand stencils that explicitly models, propagates, and aggregates uncertainty throughout the analytical pipeline. The methodology combines dual image processing, dual contour extraction, structured silhouette augmentation, model architectural diversity, and ensemble-based decision aggregation. The pipeline generates twelve plausible silhouette realizations per stencil to capture boundary uncertainties, which are processed by two ensembles of ten deep neural networks each (EfficientNet-B3 and MobileViT-S) trained on 14,036 contemporary hand samples. Furthermore, a triangulated validation scheme integrates ensemble predictions with unsupervised 2D latent-space manifold mapping (UMAP + k-NN) and explainable AI spatial attributions (LayerCAM) to ensure anatomical consistency. On contemporary data, ensemble models achieve strong classification performance, with accuracies exceeding 88% in older age groups. When applied to prehistoric stencils, the framework produces both sex predictions and confidence measures of internal agreement, enabling the distinction between morphologically stable and ambiguous cases. Convergence across ensemble predictions, latent-space structure, and interpretability analyses shows that uncertainty can become a measurable component of archaeological inference, enabling robust and reproducible decoding of ancient rock art.
