Volunteer citizen scientists are an invaluable resource for classifying large numbers of images that are used for species monitoring. Citizen science projects often rely on the “wisdom of the crowd” through majority vote methods to produce accurate classifications and assume all volunteer citizen scientists have equal ability.

We use a Bayesian framework to estimate iNaturalist NZ user accuracies and simultaneously collectively classify the observations. We calculate the probability that the inferred observation classification from the Bayesian framework is correct for each observation given the assumed true user accuracies. We refer to this probability as the classification certainty.

Our results show that 50% of images were classified by more volunteer citizen scientists than required to reach a minimal desired collective classification certainty level and more than one third of identifications were above the number required to meet the minimal desired classification certainty.

Over 60% of observations that are yet to be considered research grade have a high classification certainty that has already surpassed the desired minimal level and could therefore be upgraded to research grade with no additional identifications.

With more sophisticated collective classification methods than a simple majority vote procedure citizen science data and volunteer citizen scientists effort could be utilised more optimally.

志愿公民科学家是对用于物种监测的大量图像进行分类的宝贵资源。公民科学项目通常通过多数表决方法依靠“人群的智慧”来产生准确的分类，并假定所有志愿公民科学家具有同等的能力。

我们使用贝叶斯框架来估计iNaturalist NZ用户的准确性，并同时对观察结果进行集体分类。在给定假定的真实用户准确度的情况下，我们计算从贝叶斯框架推断的观察分类对于每个观察正确的概率。我们将此概率称为分类确定性。

我们的研究结果表明，50％的图像由更多的自愿公民科学家进行了分类，而不是达到最低期望的集体分类确定性水平所需的图像，并且超过三分之一的标识超过了满足最低期望的分类确定性所需的数量。

尚待考虑的研究等级中，有超过60％的观测结果具有较高的分类确定性，已经超过了所需的最低水平，因此可以在没有其他识别的情况下升级为研究等级。

通过比简单的多数表决程序更复杂的集体分类方法，公民科学数据和志愿者公民科学家的工作可以得到更好的利用。