## 5 Conclusions  
The Cohen's \(\kappa\) output values were high, ranging between 0.917 (Model 4) and 0.994 (Model 5), suggesting that the automated identification of jellyfish through images submitted to these classification models is generally robust and reliable. In this work, the default model parameters were used. The use of hyperparameter optimisation methods that will further fine-tune the selected model is recommended for future work. This exercise is expected to further improve on the results presented here. In terms of precision values, the five jellyfish species adopted for the current study were classified correctly in the following decreasing order; _Cotylorhiza tuberculata_ (4/5 models reported a value of 1 for precision), _Velella veella_ (3/5 models reported a value of 1 for precision), salps (2/5 models reported a value of 1 for precision), _Pelgia notiluca_ (1/5 models reported a value of 1 for precision) and _Carybdea marsupialis_ (precision values of 1 were not reported by any model). Corresponding results obtained by [PERSON] et al. [10] similarly rank _Cotylorhiza tuberculata_ (along with _Rhizostoma pulmo_, which was not considered in the current study) higher than _Pelgia notiluca_ in terms of correct identification success. These authors attribute this result to the fact that _Cotylorhiza tuberculata_ is a larger jellyfish whose body remains relatively unchanged whilst swimming, thus rendering them more amenable for correct identification, while in _Pelgia notiluca_, the relative position of the tentacles in relation to the main body (umbrella) changes to a greater extent with the movement of the animal, adopting a multitude of shapes, making it more difficult to identify. The fact that _Carybdea marsupialis_ was the least correctly identified jellyfish species might be tentatively attributed to the highly transparent nature of the species and to the high incidence of night-time (and, consequently, low-lit) images of the species, due to its nocturnal and photo-tactic habits. The lowest precision score obtained from the five classification models was that of 0.814 (_Pelgia notiluca_, Model 4), with this being one value, out of just two, which were inferior to a threshold of 0.9. Similarly, high recall and \(f_{1}\) score metric values were recorded through the implementation of the five classification models. This further substantiates the robust performance of the same classification models in correctly identifying jellyfish species from the images provided. The \(f_{1}\) score metric values obtained in the current study, ranging between 0.843 (_Pelgia notiluca_ within classification Model 4) to 1 (_Velella veella_, salps and _Cotylorhiza tuberculata_ within classification Model 5) are comparable to the same metric values reported within [10], which range between 0.936 and 0.952. It is essential for every citizen science campaign to continuously monitor and validate data quality [28]. We are confident that the results presented in this study support our upcoming aspirations to embed the image analysis protocols described therein within a smart phone app and within our campaign website to enable its broad application. This would in turn spearhead its further performance testing and subsequent optimisation through the submission of an increasing number of  
Figure 8: Sample of images that show _Velella veella_ individuals which were used for training. Figure 7: Output by classification model 5 for salps. jellyfish images and output monitoring. Additionally, the formulation of guidelines on the taking of high-quality jellyfish photos by citizen scientists in order to ensure their applicability within image analysis protocols is recommended. All three authors contributed significantly to the publishing of this work. [PERSON] drafted the manuscript, planned, and implemented the experiments. [PERSON] assisted in the methodology and model development, validation, as well as in the review and editing of the paper.