高峰期的成功：结合个人和考察范围因素的多尺度方法
Success at high peaks: a multiscale approach combining individual and expedition-wide factors
摘要：
这项工作提出了一个基于网络的数据驱动的研究，有助于登山成功的因素的组合。它同时考察了年龄、性别、经验等个人因素的影响，以及考察范围内的因素，如营地数量，夏尔巴人与付费登山者的比例等。具体来说，它将这两个视角结合到一个多尺度网络中，即，在每个探险队中，在更细的尺度上有一个由个人登山者特征组成的网络，在更粗的尺度上有一个探险队相似性网络。后者被表示为多路复用网络，其中层编码不同的因子。分析表明，由于疲劳，海拔或后勤问题而未能登顶的机会，在与重复伙伴一起攀登时，特别是对于经验丰富的登山者来说，会大大减少。此外，节点中心性表明，年轻和氧气使用的个人特征是成功的最强驱动力。此外，网络预测的学习使得能够计算内部探险网络和相应的探险成功率之间的相关性。考察范围的因素，考察规模和长度层被发现与成功率强相关。最后，社区发现上的远征相似性网络揭示了不同的社区，其中成功率的差异自然出现在社区之间。
英文摘要：
This work presents a network-based data-driven study of the combination of factors that contribute to success in mountaineering. It simultaneously examines the effects of individual factors such as age, gender, experience etc., as well as expedition-wide factors such as number of camps, ratio of sherpas to paying climbers etc. Specifically, it combines the two perspectives into a multiscale network, i.e., a network of individual climber features within each expedition at the finer scale, and an expedition similarity network on the coarser scale. The latter is represented as a multiplex network where layers encode different factors. The analysis reveals that chances of failure to summit due to fatigue, altitude or logistical problems, drastically reduce when climbing with repeat partners, especially for experienced climbers. Additionally, node-centrality indicates that individual traits of youth and oxygen use are the strongest drivers of success. Further, the learning of network projections enables computation of correlations between intra-expedition networks and corresponding expedition success rates. Of expedition-wide factors, the expedition size and length layers are found to be strongly correlated with success rate. Lastly, community detection on the expedition-similarity network reveals distinct communities where a difference in success rates naturally emerges amongst the communities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
