Wildwatch Kenya expert verified data

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail-cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail-cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy are poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photo was circulated to multiple volunteers. All volunteer classifications were aggregated...科研人员正愈发多地借助公民科学家（citizen scientists）的志愿力量，对运动触发式野外步道相机（motion-activated trail-cameras）拍摄的图像开展分类标注工作。公民科学的热度持续攀升，彰显出其在引导公众参与保护科学研究、加速处理野外相机生成的海量图像方面的潜力。尽管公民科学家的图像分类准确率会因物种而异，但其他因素对准确率的影响机制仍有待厘清。分类偏差会降低公民科学衍生数据的应用价值，也催生了制定针对性最佳实践规范以降低标注错误率的需求。本研究对比了三个依托众包公民科学家在线处理图像的项目的分类准确率：Snapshot Serengeti、Wildwatch Kenya 与 AmazonCam Tambopata。我们提出研究假设：栖息地类型与相机参数设置会对分类准确率产生影响。为评估上述影响因素，本研究将每张图像分发给多名志愿者完成标注。所有志愿者的标注结果将被汇总……