Underwater video is increasingly used to study aspects of the Great Lakes benthos including the abundance of round goby and dreissenid mussels. The introduction of these two species have resulted in major ecological shifts in the Great Lakes, but the species and their impacts have heretofore been underassessed due to limitations of monitoring methods. Underwater video (UVID) can "sample" hard bottom sites where grab samplers cannot. Efficient use of UVID data requires affordable and accurate classification and analysis tools. Deep Lake Explorer (DLE) is a web application developed to support crowdsourced classification of UVID collected in the Great Lakes. Volunteers (i.e., the crowd) used DLE to classify 199 videos collected in the Niagara River, Lake Huron, and Lake Ontario for the following attributes: round goby, Dreissena, and aquatic vegetation presence, and dominant substrate type. We compared DLE classification results to expert classification of the same videos to evaluate accuracy. Depending on the attribute, DLE had 77% (hard substrate) to 90% (vegetation presence) agreement with expert classification of videos. Detection rates, or the number of videos with an attribute detected by both volunteers and an expert divided by the number where only the expert detected the attribute, ranged from 62% (hard substrate) to 95% (soft substrate) depending on the attribute. Many factors affected accuracy, including video quality in the application, video processing, abundance of species of interest, volunteer experience, and task complexity. Crowdsourcing tools like DLE can increase timeliness and decrease costs but may come with tradeoffs in accuracy and completeness.

中文翻译：

---

Deep Lake Explorer：用于众包劳伦森五大湖底栖水下视频分类的网络应用程序

水下视频越来越多地用于研究五大湖底栖动物的各个方面，包括丰富的圆形虾虎鱼和德莱森贻贝。这两个物种的引入导致了五大湖的重大生态变化，但由于监测方法的局限性，迄今为止对这些物种及其影响的评估一直不足。水下视频 (UVID) 可以“采样”硬底站点，而抓取采样器则不能。有效使用 UVID 数据需要经济实惠且准确的分类和分析工具。Deep Lake Explorer (DLE) 是一个 Web 应用程序，旨在支持对五大湖中收集的 UVID 进行众包分类。志愿者（即人群）使用 DLE 对在尼亚加拉河、休伦湖和安大略湖收集的 199 个视频进行了以下属性分类：圆形虾虎鱼、德雷塞纳、和水生植被的存在，以及主要的基质类型。我们将 DLE 分类结果与相同视频的专家分类结果进行比较，以评估准确性。根据属性，DLE 与专家视频分类的一致性为 77%（硬底物）到 90%（植被存在）。检测率，或由志愿者和专家检测到的具有属性的视频数量除以只有专家检测到该属性的数量，范围从 62%（硬基板）到 95%（软基板），具体取决于属性。许多因素会影响准确性，包括应用程序中的视频质量、视频处理、感兴趣物种的丰富程度、志愿者经验和任务复杂性。DLE 等众包工具可以提高及时性并降低成本，但可能会在准确性和完整性方面进行权衡。