AI 中文总结
该研究提出效价感知社交网络框架，基于计算机视觉分析奶牛互动，发现不同效价的互动形成对比社交层，汇总互动计数会掩盖网络行为构成，为牲畜监测提供新方法。
AI 中文摘要
社会关系影响资源获取、冲突暴露和群体稳定性，然而自动化牲畜监测通常将行为视为孤立事件。本文提出一种效价感知社交网络框架，该框架将视频衍生的互动转化为群体层面的亲和性与攻击性组织表征。一种基于姿态的计算机视觉流程分析了某商业奶牛场挤奶前区域7小时39分钟的连续视频，经质量控制后，1414个候选互动中保留1183个，涉及36头奶牛和177个二元组。在对198个流程检测片段的预测类别平衡审计中，自动标签与人工标签的一致性达82.8%，未加权审计样本宏F1为0.872，这些数值描述审计样本而非患病率加权或端到端部署性能。聚合网络是连通的（密度=0.281；传递性=0.513；平均路径长度=1.88），预测的亲和性事件形成5个算法社区（模块度Q=0.429）。在观测区域内，预测的攻击性互动占保留事件的72.4%和互动时长的76.0%，拥有最多伙伴的奶牛并非具有最高介数中心性的个体。按预测效价对事件进行划分，产生了具有对比性边集、社区划分和个体位置的不同亲和性与攻击性层。因此，汇总的互动计数可能掩盖观测网络的行为构成，效价感知分析为检验与竞争、亲和性及福利相关变化的假设提供了框架，但在用作福利或健康指标前需进行纵向验证。
英文摘要
Social relationships shape access to resources, exposure to conflict and group stability, yet automated livestock monitoring typically treats behaviour as isolated events. Here, we present a valence-aware social-network framework that transforms video-derived interactions into herd-level representations of affiliative and agonistic organization. A pose-based computer-vision pipeline analysed 7 h 39 min of continuous video from the pre-milking area of one commercial dairy farm. After quality control, 1,183 of 1,414 candidate interactions remained, involving 36 cows and 177 dyads. In a predicted-class-balanced audit of 198 pipeline-detected clips, automated and manual labels agreed in 82.8% of cases, with an unweighted audit-sample macro-F1 of 0.872. These values describe the audited sample rather than prevalence-weighted or end-to-end deployment performance. The aggregated network was connected (density = 0.281; transitivity = 0.513; mean path length = 1.88), and predicted affiliative events formed five algorithmic communities (modularity Q = 0.429). Within the observed zone, predicted agonistic interactions comprised 72.4% of retained events and 76.0% of interaction duration. The cow with the most partners did not have the highest betweenness centrality. Separating events by predicted valence produced descriptively different affiliative and agonistic layers, with contrasting edge sets, community partitions and individual positions. Thus, pooled interaction counts can obscure the behavioural composition of an observed network. Valence-aware analysis provides a framework for testing hypotheses about competition, affiliation and welfare-relevant change, while requiring longitudinal validation before use as a welfare or health indicator.
Comments29 pages, 8 figures, 9 tables
